Month: August 2024
With Simulink Embedded Coder is it possible to change base workspace variables?
Hi all,
We are developing a standalone application for an inverter using MATLAB/Simulink R2022b with Embedded Coder and TIs package for C2000 development. Our target is TIs Launchpad XL based on the F28379D CPU. My goal is to write a Simulink model that can modify onboard flash during runtime, so when power is lost certain calibration values are maintained.
My model currently implements a Simulink reference example: https://nl.mathworks.com/matlabcentral/fileexchange/92788-simulink-reference-application-examples-for-ti-c2000?s_tid=FX_rc1_behav
This works well, but has a major downside: it is only possible to modify the variables in the matlab workspace and then press Ctrl+d to update them on the target machine while connected in external mode over a serial connection. For our devices in the field, we want to get rid of the mandatory external mode and achieve the same over CAN bus.
I have already tried a bunch of things, but none of them yield the desired behavior:
set_param(): will not work as the calibration values are stored in the MATLAB base workspace. I am not changing blocks.
assignin(): gives the following error: The function <function_name> is not supported for code generation. Declaring the function as extrinsic via coder.extrinsic does not solve the problem.
Parameter Writer block: In R2022b this can only modify variables in the model workspace, so I moved the flash related parameters accordingly. However, somehow the parameter writer block just won’t find them. My guess is that it is because the parameters are defined in the base workspace as a specific storage class based on the tic2000demospkg.Parameter. In the Code Mappings interface they therefore show up as ‘External Parameter Objects’ instead of ‘Model Parameters.’ Based on this article: https://blogs.mathworks.com/simulink/2023/09/27/signals-vs-parameters-in-simulink-and-the-parameter-writer-block/
MATLAB function block: I was hoping the parameters I defined in the base workspace could be called in a global context. Calling the matlab routine from Simulink showed me this is unfortunately not the case. Implicitly the variable was defined in local context and base variable remained unchanged. Inspecting gave clarity on this.
For a fact I know that the variables that I want to tune are defined with the mp_ prefix because I configured the identifier for the custom storage class. However, I don’t understand on how I want to change these.
Is it at all possible to modify variables? Can someone give me a heads up on how?
Thanks a lot in advance!
Kind regards,
RemiHi all,
We are developing a standalone application for an inverter using MATLAB/Simulink R2022b with Embedded Coder and TIs package for C2000 development. Our target is TIs Launchpad XL based on the F28379D CPU. My goal is to write a Simulink model that can modify onboard flash during runtime, so when power is lost certain calibration values are maintained.
My model currently implements a Simulink reference example: https://nl.mathworks.com/matlabcentral/fileexchange/92788-simulink-reference-application-examples-for-ti-c2000?s_tid=FX_rc1_behav
This works well, but has a major downside: it is only possible to modify the variables in the matlab workspace and then press Ctrl+d to update them on the target machine while connected in external mode over a serial connection. For our devices in the field, we want to get rid of the mandatory external mode and achieve the same over CAN bus.
I have already tried a bunch of things, but none of them yield the desired behavior:
set_param(): will not work as the calibration values are stored in the MATLAB base workspace. I am not changing blocks.
assignin(): gives the following error: The function <function_name> is not supported for code generation. Declaring the function as extrinsic via coder.extrinsic does not solve the problem.
Parameter Writer block: In R2022b this can only modify variables in the model workspace, so I moved the flash related parameters accordingly. However, somehow the parameter writer block just won’t find them. My guess is that it is because the parameters are defined in the base workspace as a specific storage class based on the tic2000demospkg.Parameter. In the Code Mappings interface they therefore show up as ‘External Parameter Objects’ instead of ‘Model Parameters.’ Based on this article: https://blogs.mathworks.com/simulink/2023/09/27/signals-vs-parameters-in-simulink-and-the-parameter-writer-block/
MATLAB function block: I was hoping the parameters I defined in the base workspace could be called in a global context. Calling the matlab routine from Simulink showed me this is unfortunately not the case. Implicitly the variable was defined in local context and base variable remained unchanged. Inspecting gave clarity on this.
For a fact I know that the variables that I want to tune are defined with the mp_ prefix because I configured the identifier for the custom storage class. However, I don’t understand on how I want to change these.
Is it at all possible to modify variables? Can someone give me a heads up on how?
Thanks a lot in advance!
Kind regards,
Remi Hi all,
We are developing a standalone application for an inverter using MATLAB/Simulink R2022b with Embedded Coder and TIs package for C2000 development. Our target is TIs Launchpad XL based on the F28379D CPU. My goal is to write a Simulink model that can modify onboard flash during runtime, so when power is lost certain calibration values are maintained.
My model currently implements a Simulink reference example: https://nl.mathworks.com/matlabcentral/fileexchange/92788-simulink-reference-application-examples-for-ti-c2000?s_tid=FX_rc1_behav
This works well, but has a major downside: it is only possible to modify the variables in the matlab workspace and then press Ctrl+d to update them on the target machine while connected in external mode over a serial connection. For our devices in the field, we want to get rid of the mandatory external mode and achieve the same over CAN bus.
I have already tried a bunch of things, but none of them yield the desired behavior:
set_param(): will not work as the calibration values are stored in the MATLAB base workspace. I am not changing blocks.
assignin(): gives the following error: The function <function_name> is not supported for code generation. Declaring the function as extrinsic via coder.extrinsic does not solve the problem.
Parameter Writer block: In R2022b this can only modify variables in the model workspace, so I moved the flash related parameters accordingly. However, somehow the parameter writer block just won’t find them. My guess is that it is because the parameters are defined in the base workspace as a specific storage class based on the tic2000demospkg.Parameter. In the Code Mappings interface they therefore show up as ‘External Parameter Objects’ instead of ‘Model Parameters.’ Based on this article: https://blogs.mathworks.com/simulink/2023/09/27/signals-vs-parameters-in-simulink-and-the-parameter-writer-block/
MATLAB function block: I was hoping the parameters I defined in the base workspace could be called in a global context. Calling the matlab routine from Simulink showed me this is unfortunately not the case. Implicitly the variable was defined in local context and base variable remained unchanged. Inspecting gave clarity on this.
For a fact I know that the variables that I want to tune are defined with the mp_ prefix because I configured the identifier for the custom storage class. However, I don’t understand on how I want to change these.
Is it at all possible to modify variables? Can someone give me a heads up on how?
Thanks a lot in advance!
Kind regards,
Remi ti, embedded coder, simulink, variables, code generation, matlab code MATLAB Answers — New Questions
How to keep the order in plotting scatter plot with categorical variables
Hi,
I am using scatter (b,a) where a is categorical variable and b is double. The plot shows a different order than in my list in a. How do I keep the same order in the plot?Hi,
I am using scatter (b,a) where a is categorical variable and b is double. The plot shows a different order than in my list in a. How do I keep the same order in the plot? Hi,
I am using scatter (b,a) where a is categorical variable and b is double. The plot shows a different order than in my list in a. How do I keep the same order in the plot? scatter, plot, categorical MATLAB Answers — New Questions
Why do I get the error “Unable to resolve the name com.mathworks.toolbox.distcomp.ui.widget.SupportedDataTypes.STRING” when running configCluster?
Why do I get the error "Unable to resolve the name com.mathworks.toolbox.distcomp.ui.widget.SupportedDataTypes.STRING" when running configCluster?Why do I get the error "Unable to resolve the name com.mathworks.toolbox.distcomp.ui.widget.SupportedDataTypes.STRING" when running configCluster? Why do I get the error "Unable to resolve the name com.mathworks.toolbox.distcomp.ui.widget.SupportedDataTypes.STRING" when running configCluster? MATLAB Answers — New Questions
Using the trigonometric Fourier series to develop MATLAB code to confirm correctness
fs = 2/5 + sum(2/(n*pi)*sin(2*pi/5*n)*cos(2*pi/5*n*t))
x(t) = sum(rect((t-5*n)/2))
I really don’t know how to plot two function. Please help me.fs = 2/5 + sum(2/(n*pi)*sin(2*pi/5*n)*cos(2*pi/5*n*t))
x(t) = sum(rect((t-5*n)/2))
I really don’t know how to plot two function. Please help me. fs = 2/5 + sum(2/(n*pi)*sin(2*pi/5*n)*cos(2*pi/5*n*t))
x(t) = sum(rect((t-5*n)/2))
I really don’t know how to plot two function. Please help me. trigonometric fourier series MATLAB Answers — New Questions
Status reports intune
I am using the API “deviceManagement/reports/getUserInstallStatusReport” with the HTTP POST method, and the request body includes:
Intune returns a maximum of 50 records, but some applications are installed by more than 5000 users. To check if a specific user has installed this application and to get the installation status report for this application on their device, I have to loop through the results by incrementing the skip value by 50. This process takes a lot of time and often results in a 429 status (Too Many Requests).
Is there any way to filter by UserName directly? Currently, this doesn’t seem to be supported.
I am using the API “deviceManagement/reports/getUserInstallStatusReport” with the HTTP POST method, and the request body includes: { “select”: [ “UserName”, “UserPrincipalName”, “FailedCount”, “InstalledCount”, “PendingInstallCount”, “NotInstalledCount” ], “skip”: 0, “top”: 50, “filter”: “(ApplicationId eq ‘XXXXXXXX-XXXX-XXXX-XXXX-XXXXXXXXXXXX’)” } Intune returns a maximum of 50 records, but some applications are installed by more than 5000 users. To check if a specific user has installed this application and to get the installation status report for this application on their device, I have to loop through the results by incrementing the skip value by 50. This process takes a lot of time and often results in a 429 status (Too Many Requests).Is there any way to filter by UserName directly? Currently, this doesn’t seem to be supported. Read More
Azure Monitoring Agent Extension – no update
Hello, I am using Azure Arc along with the AzureMonitorLinuxAgent and AzureMonitorWindowsAgent extensions.
However, I’ve encountered multiple instances where the version listed in the changelog for the Azure Monitor Agent extension (Azure Monitor Agent extension versions – Azure Monitor | Microsoft Learn) is newer than the version I can see in the portal – also with azure cli or powershell, the newest version is missing – i only see the previous one.
Has anyone else experienced this issue? Last time they had to redeploy something in westeurope
But i guess this is no coincidence
Thanks if somebody can share there experience or can help…
Hello, I am using Azure Arc along with the AzureMonitorLinuxAgent and AzureMonitorWindowsAgent extensions.However, I’ve encountered multiple instances where the version listed in the changelog for the Azure Monitor Agent extension (Azure Monitor Agent extension versions – Azure Monitor | Microsoft Learn) is newer than the version I can see in the portal – also with azure cli or powershell, the newest version is missing – i only see the previous one.Has anyone else experienced this issue? Last time they had to redeploy something in westeuropeBut i guess this is no coincidenceThanks if somebody can share there experience or can help… Read More
ASP.NET MVC4 to ASP.NET Core 8
Question:
Should replace our DevExpress controls with supported .NET 8 controls?
What are our options going forward?
Background
We are migrating ASP.NET 4 MVC to ASP.NET Core 8. Our application has DevExpress controls which underneath uses System.Web(.NET Framework 4.8). DevExpress controls use System.Web assembly or extend System.Web.UI.Control from that library. Regarding documentation ASP.NET Core 8 doesn’t support System.Web.UI.Controls. And our code doesn’t compile because of that.
Question: Should replace our DevExpress controls with supported .NET 8 controls?What are our options going forward? BackgroundWe are migrating ASP.NET 4 MVC to ASP.NET Core 8. Our application has DevExpress controls which underneath uses System.Web(.NET Framework 4.8). DevExpress controls use System.Web assembly or extend System.Web.UI.Control from that library. Regarding documentation ASP.NET Core 8 doesn’t support System.Web.UI.Controls. And our code doesn’t compile because of that. Read More
Lookup and Iterate
I have a dataset that i would like to use to lookup but I need to iterate between set values such as 1:03 that falls between 1:00 and 1:05 AFTER my time of concentration is greater than 1 hr. Any help would be greatly appreciated.
D=0.48Duration of rainfall event (hours)SI=0.02Slope of longest flow path (elevation difference between 10% and 85% of path length, divided by .75L)Tc=00:29Time of concentration (This is the cell that is looked up on the table Column A)*Tc is calculated using equation that assumes grass and aggregate-lined roads and ditches Recurrence Interval
(Intensity) first lookup value
=LOOKUP(B19,’Riley County Rainfall Intensity’!A2:A117,’Riley County Rainfall Intensity’!B2:B117)
(Discharge)
(inches/hr)Qt (cfs)2 year2.54 132 5 year3.1926510 year3.7539025 year4.5659850 year4.86698100 year5.89919
Table Referenced
Time2yr5yr10yr25yr50yr100yr00:055.797.268.5310.3711.8513.3800:065.356.717.899.5810.9512.3600:0756.277.378.9510.2311.5500:084.715.96.948.439.6310.8800:094.465.596.577.989.1210.300:104.245.316.257.598.679.7900:114.045.075.967.248.279.3400:123.864.855.76.927.918.9300:133.714.655.476.657.598.5700:143.574.485.266.47.318.2500:153.444.325.086.177.057.9600:163.344.194.935.996.847.7200:173.254.084.795.826.657.5100:183.163.974.675.676.477.3100:193.093.874.555.536.317.1300:203.013.784.445.46.166.9600:212.943.694.345.276.026.800:222.883.614.245.155.896.6500:232.813.534.155.045.766.500:242.753.464.064.945.646.3700:252.73.393.984.845.536.2400:262.643.323.94.745.426.1200:272.593.253.834.655.31600:282.543.193.754.565.215.8900:292.493.133.684.485.125.7800:302.443.073.614.395.025.6800:312.43.023.554.324.945.5800:322.362.973.494.254.865.4900:332.322.923.434.184.785.4100:342.282.873.384.114.715.3200:352.252.823.324.054.635.2400:362.212.783.273.984.565.1600:372.182.733.223.924.495.0900:382.142.693.173.864.425.0100:392.112.653.123.84.364.9400:402.082.613.073.754.34.8700:412.052.573.033.694.234.800:422.022.532.983.644.174.7300:431.992.492.943.594.124.6700:441.962.462.93.544.064.6100:451.932.422.863.4944.5400:461.92.392.823.443.954.4800:471.882.362.783.393.94.4300:481.852.322.743.353.854.3700:491.822.292.73.313.84.3100:501.82.262.673.263.754.2600:511.782.232.633.223.74.2100:521.752.22.63.183.664.1600:531.732.172.573.143.614.1100:541.712.152.533.13.574.0600:551.692.122.53.063.524.0100:561.672.092.473.033.483.9600:571.652.072.442.993.443.9200:581.632.042.412.953.43.8700:591.612.022.382.923.363.8301:001.5922.362.893.323.7801:051.511.892.242.743.163.601:101.441.82.132.613.013.4301:151.371.722.032.52.883.2801:201.311.651.942.392.753.1401:251.261.581.862.292.643.0201:301.211.511.792.22.542.901:351.161.461.722.122.452.7901:401.121.41.662.042.362.6901:451.081.351.61.972.282.601:501.041.311.551.912.22.5201:551.011.271.51.852.132.4402:000.981.231.451.792.072.3702:050.951.191.411.742.012.302:100.921.161.371.691.962.2402:150.91.131.341.651.912.1802:200.871.11.31.61.862.1302:250.851.071.271.561.812.0802:300.831.041.241.531.772.0302:350.811.021.211.491.731.9802:400.7911.181.461.691.9402:450.770.971.151.421.651.902:500.760.951.131.391.621.8602:550.740.931.11.361.581.8203:000.730.911.081.341.551.7803:150.680.861.021.261.461.6803:300.650.810.971.191.391.5903:450.610.770.921.131.321.5204:000.580.740.871.081.261.4404:150.560.70.831.031.21.3804:300.530.670.80.991.151.3204:450.510.650.770.951.11.2705:000.490.620.740.911.061.2205:150.470.60.710.881.021.1705:300.460.580.680.850.981.1305:450.440.560.660.820.951.0906:000.430.540.640.790.921.0506:300.40.510.60.740.860.9907:000.380.480.570.70.810.9307:300.360.450.540.660.770.8808:000.340.430.510.630.730.8408:300.320.410.490.60.70.809:000.310.390.470.570.660.7609:300.30.380.450.550.630.7210:000.290.360.430.530.610.6910:300.270.350.410.510.580.6611:000.260.330.40.490.560.6411:300.250.320.380.470.540.6112:000.250.310.370.450.520.5913:000.230.290.340.420.490.5514:000.220.270.320.40.460.5215:000.210.260.310.370.430.4916:000.190.250.290.350.410.4617:000.190.230.270.340.380.4418:000.180.220.260.320.370.4119:000.170.210.250.30.350.3920:000.160.20.240.290.330.3821:000.160.190.230.280.320.3622:000.150.190.220.270.310.3523:000.140.180.210.260.290.3300:000.140.170.20.250.280.32
I have a dataset that i would like to use to lookup but I need to iterate between set values such as 1:03 that falls between 1:00 and 1:05 AFTER my time of concentration is greater than 1 hr. Any help would be greatly appreciated. D=0.48Duration of rainfall event (hours)SI=0.02Slope of longest flow path (elevation difference between 10% and 85% of path length, divided by .75L)Tc=00:29Time of concentration (This is the cell that is looked up on the table Column A)*Tc is calculated using equation that assumes grass and aggregate-lined roads and ditches Recurrence Interval(Intensity) first lookup value =LOOKUP(B19,’Riley County Rainfall Intensity’!A2:A117,’Riley County Rainfall Intensity’!B2:B117)(Discharge)(inches/hr)Qt (cfs)2 year2.54 132 5 year3.1926510 year3.7539025 year4.5659850 year4.86698100 year5.89919 Table Referenced Time2yr5yr10yr25yr50yr100yr00:055.797.268.5310.3711.8513.3800:065.356.717.899.5810.9512.3600:0756.277.378.9510.2311.5500:084.715.96.948.439.6310.8800:094.465.596.577.989.1210.300:104.245.316.257.598.679.7900:114.045.075.967.248.279.3400:123.864.855.76.927.918.9300:133.714.655.476.657.598.5700:143.574.485.266.47.318.2500:153.444.325.086.177.057.9600:163.344.194.935.996.847.7200:173.254.084.795.826.657.5100:183.163.974.675.676.477.3100:193.093.874.555.536.317.1300:203.013.784.445.46.166.9600:212.943.694.345.276.026.800:222.883.614.245.155.896.6500:232.813.534.155.045.766.500:242.753.464.064.945.646.3700:252.73.393.984.845.536.2400:262.643.323.94.745.426.1200:272.593.253.834.655.31600:282.543.193.754.565.215.8900:292.493.133.684.485.125.7800:302.443.073.614.395.025.6800:312.43.023.554.324.945.5800:322.362.973.494.254.865.4900:332.322.923.434.184.785.4100:342.282.873.384.114.715.3200:352.252.823.324.054.635.2400:362.212.783.273.984.565.1600:372.182.733.223.924.495.0900:382.142.693.173.864.425.0100:392.112.653.123.84.364.9400:402.082.613.073.754.34.8700:412.052.573.033.694.234.800:422.022.532.983.644.174.7300:431.992.492.943.594.124.6700:441.962.462.93.544.064.6100:451.932.422.863.4944.5400:461.92.392.823.443.954.4800:471.882.362.783.393.94.4300:481.852.322.743.353.854.3700:491.822.292.73.313.84.3100:501.82.262.673.263.754.2600:511.782.232.633.223.74.2100:521.752.22.63.183.664.1600:531.732.172.573.143.614.1100:541.712.152.533.13.574.0600:551.692.122.53.063.524.0100:561.672.092.473.033.483.9600:571.652.072.442.993.443.9200:581.632.042.412.953.43.8700:591.612.022.382.923.363.8301:001.5922.362.893.323.7801:051.511.892.242.743.163.601:101.441.82.132.613.013.4301:151.371.722.032.52.883.2801:201.311.651.942.392.753.1401:251.261.581.862.292.643.0201:301.211.511.792.22.542.901:351.161.461.722.122.452.7901:401.121.41.662.042.362.6901:451.081.351.61.972.282.601:501.041.311.551.912.22.5201:551.011.271.51.852.132.4402:000.981.231.451.792.072.3702:050.951.191.411.742.012.302:100.921.161.371.691.962.2402:150.91.131.341.651.912.1802:200.871.11.31.61.862.1302:250.851.071.271.561.812.0802:300.831.041.241.531.772.0302:350.811.021.211.491.731.9802:400.7911.181.461.691.9402:450.770.971.151.421.651.902:500.760.951.131.391.621.8602:550.740.931.11.361.581.8203:000.730.911.081.341.551.7803:150.680.861.021.261.461.6803:300.650.810.971.191.391.5903:450.610.770.921.131.321.5204:000.580.740.871.081.261.4404:150.560.70.831.031.21.3804:300.530.670.80.991.151.3204:450.510.650.770.951.11.2705:000.490.620.740.911.061.2205:150.470.60.710.881.021.1705:300.460.580.680.850.981.1305:450.440.560.660.820.951.0906:000.430.540.640.790.921.0506:300.40.510.60.740.860.9907:000.380.480.570.70.810.9307:300.360.450.540.660.770.8808:000.340.430.510.630.730.8408:300.320.410.490.60.70.809:000.310.390.470.570.660.7609:300.30.380.450.550.630.7210:000.290.360.430.530.610.6910:300.270.350.410.510.580.6611:000.260.330.40.490.560.6411:300.250.320.380.470.540.6112:000.250.310.370.450.520.5913:000.230.290.340.420.490.5514:000.220.270.320.40.460.5215:000.210.260.310.370.430.4916:000.190.250.290.350.410.4617:000.190.230.270.340.380.4418:000.180.220.260.320.370.4119:000.170.210.250.30.350.3920:000.160.20.240.290.330.3821:000.160.190.230.280.320.3622:000.150.190.220.270.310.3523:000.140.180.210.260.290.3300:000.140.170.20.250.280.32 Read More
SharePoint List JSON Formatting – Layout elements horizontally instead of vertically
How to I change the JSON below so that the elements shown in the screenshot are laid out horizontally instead of vertically?
Thank you,
Ed
{
“$schema”: “https://developer.microsoft.com/json-schemas/sp/view-formatting.schema.json”,
“hideSelection”: true,
“hideColumnHeader”: false,
“rowFormatter”: {
“elmType”: “div”,
“attributes”: {
“class”: “sp-row-card”
},
“children”: [
{
“elmType”: “div”,
“style”: {
“margin-left”: “10px”,
“margin-right”: “25px”,
“height”: “130px”
},
“children”: [
{
“elmType”: “img”,
“attributes”: {
“src”: “@thumbnail.large”,
“title”: “[$Title]”
},
“style”: {
“border”: “1px solid #808080”,
“border-radius”: “2%”,
“width”: “100%”
}
}
]
},
{
“elmType”: “div”,
“style”: {
“text-align”: “left”,
“border-left”: “2px solid rgba(200, 200, 200, 0.5)”,
“padding-left”: “2px”
},
“children”: [
{
“elmType”: “div”,
“attributes”: {
“class”: “sp-row-title”
},
“style”: {
“font-weight”: “semi-bold”,
“font-size”: “x-large”,
“margin-left”: “10px”
},
“txtContent”: “=if([$TalkTitle] == ”, [$FileLeafRef], [$TalkTitle])”
},
{
“elmType”: “div”,
“attributes”: {
“class”: “sp-row-listPadding”
},
“style”: {
“margin-left”: “10px”,
“font-size”: “larger”
},
“children”: [
{
“elmType”: “span”,
“style”: {
“font-weight”: “bold”
},
“txtContent”: “Duration: “
},
{
“elmType”: “span”,
“txtContent”: “[$Duration]”
}
]
},
{
“elmType”: “div”,
“attributes”: {
“class”: “sp-row-listPadding”
},
“style”: {
“margin-left”: “10px”,
“font-size”: “larger”
},
“children”: [
{
“elmType”: “span”,
“style”: {
“font-weight”: “bold”
},
“txtContent”: “DDD Year: “
},
{
“elmType”: “span”,
“txtContent”: “[$DDDYear]”
}
]
},
{
“elmType”: “div”,
“attributes”: {
“class”: “sp-row-listPadding”
},
“style”: {
“margin-left”: “10px”,
“font-size”: “larger”
},
“children”: [
{
“elmType”: “span”,
“style”: {
“font-weight”: “bold”
},
“txtContent”: “Speaker Name: “
},
{
“elmType”: “span”,
“txtContent”: “[$SpeakerName]”
}
]
},
{
“elmType”: “div”,
“attributes”: {
“class”: “sp-row-listPadding”
},
“style”: {
“margin-left”: “10px”,
“font-size”: “larger”
},
“children”: [
{
“elmType”: “span”,
“style”: {
“font-weight”: “bold”
},
“txtContent”: “Business Area: “
},
{
“elmType”: “span”,
“txtContent”: “[$BusinessArea]”
}
]
},
{
“elmType”: “div”,
“attributes”: {
“class”: “sp-row-listPadding”
},
“style”: {
“margin-left”: “10px”,
“font-size”: “larger”
},
“children”: [
{
“elmType”: “span”,
“style”: {
“font-weight”: “bold”
},
“txtContent”: “Product Name: “
},
{
“elmType”: “span”,
“txtContent”: “[$ProductName]”
}
]
},
{
“elmType”: “div”,
“attributes”: {
“class”: “sp-row-listPadding”
},
“style”: {
“margin-left”: “10px”,
“font-size”: “larger”
},
“children”: [
{
“elmType”: “span”,
“style”: {
“font-weight”: “bold”
},
“txtContent”: “General Subject/Concept: “
},
{
“elmType”: “span”,
“txtContent”: “[$GeneralSubject_x002f_Concept]”
}
]
},
{
“elmType”: “button”,
“customRowAction”: {
“action”: “defaultClick”
},
“style”: {
“width”: “150px”,
“margin-left”: “10px”,
“display”: “span”
},
“txtContent”: “Watch Now”,
“attributes”: {
“class”: “sp-row-button ms-bgColor-purple sp-row-button ms-bgColor-purpleDark–hover ms-fontWeight-semibold ms-fontColor-white”
}
},
{
“elmType”: “button”,
“customRowAction”: {
“action”: “share”
},
“style”: {
“width”: “150px”,
“margin-left”: “10px”
},
“txtContent”: “Share”,
“attributes”: {
“class”: “sp-row-button ms-bgColor-grey sp-row-button ms-bgColor-purpleDark–hover ms-fontColor-white–hover”
}
},
{
“elmType”: “button”,
“customRowAction”: {
“action”: “editProps”
},
“style”: {
“width”: “150px”,
“margin-left”: “10px”,
“display”: “span”
},
“txtContent”: “View Details”,
“attributes”: {
“class”: “sp-row-button ms-bgColor-grey sp-row-button ms-bgColor-purpleDark–hover ms-fontColor-white–hover”
}
}
]
}
]
}
}
How to I change the JSON below so that the elements shown in the screenshot are laid out horizontally instead of vertically? Thank you,Ed{
“$schema”: “https://developer.microsoft.com/json-schemas/sp/view-formatting.schema.json”,
“hideSelection”: true,
“hideColumnHeader”: false,
“rowFormatter”: {
“elmType”: “div”,
“attributes”: {
“class”: “sp-row-card”
},
“children”: [
{
“elmType”: “div”,
“style”: {
“margin-left”: “10px”,
“margin-right”: “25px”,
“height”: “130px”
},
“children”: [
{
“elmType”: “img”,
“attributes”: {
“src”: “@thumbnail.large”,
“title”: “[$Title]”
},
“style”: {
“border”: “1px solid #808080”,
“border-radius”: “2%”,
“width”: “100%”
}
}
]
},
{
“elmType”: “div”,
“style”: {
“text-align”: “left”,
“border-left”: “2px solid rgba(200, 200, 200, 0.5)”,
“padding-left”: “2px”
},
“children”: [
{
“elmType”: “div”,
“attributes”: {
“class”: “sp-row-title”
},
“style”: {
“font-weight”: “semi-bold”,
“font-size”: “x-large”,
“margin-left”: “10px”
},
“txtContent”: “=if([$TalkTitle] == ”, [$FileLeafRef], [$TalkTitle])”
},
{
“elmType”: “div”,
“attributes”: {
“class”: “sp-row-listPadding”
},
“style”: {
“margin-left”: “10px”,
“font-size”: “larger”
},
“children”: [
{
“elmType”: “span”,
“style”: {
“font-weight”: “bold”
},
“txtContent”: “Duration: “
},
{
“elmType”: “span”,
“txtContent”: “[$Duration]”
}
]
},
{
“elmType”: “div”,
“attributes”: {
“class”: “sp-row-listPadding”
},
“style”: {
“margin-left”: “10px”,
“font-size”: “larger”
},
“children”: [
{
“elmType”: “span”,
“style”: {
“font-weight”: “bold”
},
“txtContent”: “DDD Year: “
},
{
“elmType”: “span”,
“txtContent”: “[$DDDYear]”
}
]
},
{
“elmType”: “div”,
“attributes”: {
“class”: “sp-row-listPadding”
},
“style”: {
“margin-left”: “10px”,
“font-size”: “larger”
},
“children”: [
{
“elmType”: “span”,
“style”: {
“font-weight”: “bold”
},
“txtContent”: “Speaker Name: “
},
{
“elmType”: “span”,
“txtContent”: “[$SpeakerName]”
}
]
},
{
“elmType”: “div”,
“attributes”: {
“class”: “sp-row-listPadding”
},
“style”: {
“margin-left”: “10px”,
“font-size”: “larger”
},
“children”: [
{
“elmType”: “span”,
“style”: {
“font-weight”: “bold”
},
“txtContent”: “Business Area: “
},
{
“elmType”: “span”,
“txtContent”: “[$BusinessArea]”
}
]
},
{
“elmType”: “div”,
“attributes”: {
“class”: “sp-row-listPadding”
},
“style”: {
“margin-left”: “10px”,
“font-size”: “larger”
},
“children”: [
{
“elmType”: “span”,
“style”: {
“font-weight”: “bold”
},
“txtContent”: “Product Name: “
},
{
“elmType”: “span”,
“txtContent”: “[$ProductName]”
}
]
},
{
“elmType”: “div”,
“attributes”: {
“class”: “sp-row-listPadding”
},
“style”: {
“margin-left”: “10px”,
“font-size”: “larger”
},
“children”: [
{
“elmType”: “span”,
“style”: {
“font-weight”: “bold”
},
“txtContent”: “General Subject/Concept: “
},
{
“elmType”: “span”,
“txtContent”: “[$GeneralSubject_x002f_Concept]”
}
]
},
{
“elmType”: “button”,
“customRowAction”: {
“action”: “defaultClick”
},
“style”: {
“width”: “150px”,
“margin-left”: “10px”,
“display”: “span”
},
“txtContent”: “Watch Now”,
“attributes”: {
“class”: “sp-row-button ms-bgColor-purple sp-row-button ms-bgColor-purpleDark–hover ms-fontWeight-semibold ms-fontColor-white”
}
},
{
“elmType”: “button”,
“customRowAction”: {
“action”: “share”
},
“style”: {
“width”: “150px”,
“margin-left”: “10px”
},
“txtContent”: “Share”,
“attributes”: {
“class”: “sp-row-button ms-bgColor-grey sp-row-button ms-bgColor-purpleDark–hover ms-fontColor-white–hover”
}
},
{
“elmType”: “button”,
“customRowAction”: {
“action”: “editProps”
},
“style”: {
“width”: “150px”,
“margin-left”: “10px”,
“display”: “span”
},
“txtContent”: “View Details”,
“attributes”: {
“class”: “sp-row-button ms-bgColor-grey sp-row-button ms-bgColor-purpleDark–hover ms-fontColor-white–hover”
}
}
]
}
]
}
} Read More
New Blog | eDiscovery launches a modern, intuitive user experience
By ninachen
This month, we have launched a redesigned Microsoft Purview eDiscovery product experience in public preview. This improved user experience revolutionizes your data search, review and export tasks within eDiscovery. Our new user-friendly and feature-rich eDiscovery experience is not just about finding and preserving data, it’s about doing it with unprecedented efficiency and ease. The modern user experience of eDiscovery addresses some long-standing customer requests, such as enhanced search capabilities with MessageID, Sensitive Information Types (SITs) and sensitivity labels. It also introduces innovative features like draft query with Copilot and search using audit log. These changes, driven by customer feedback and our commitment to innovation, offer tangible value by saving time and reducing costs in the eDiscovery process.
The new eDiscovery experience is exclusively available in the Microsoft Purview portal. The new Microsoft Purview portal is a unified platform that streamlines data governance, data security, and data compliance across your entire data estate. It offers a more intuitive experience, allowing users to easily navigate and manage their compliance needs.
Read the full post here: eDiscovery launches a modern, intuitive user experience
By ninachen
This month, we have launched a redesigned Microsoft Purview eDiscovery product experience in public preview. This improved user experience revolutionizes your data search, review and export tasks within eDiscovery. Our new user-friendly and feature-rich eDiscovery experience is not just about finding and preserving data, it’s about doing it with unprecedented efficiency and ease. The modern user experience of eDiscovery addresses some long-standing customer requests, such as enhanced search capabilities with MessageID, Sensitive Information Types (SITs) and sensitivity labels. It also introduces innovative features like draft query with Copilot and search using audit log. These changes, driven by customer feedback and our commitment to innovation, offer tangible value by saving time and reducing costs in the eDiscovery process.
The new eDiscovery experience is exclusively available in the Microsoft Purview portal. The new Microsoft Purview portal is a unified platform that streamlines data governance, data security, and data compliance across your entire data estate. It offers a more intuitive experience, allowing users to easily navigate and manage their compliance needs.
Read the full post here: eDiscovery launches a modern, intuitive user experience Read More
New Blog | Face Check is now generally available
By Ankur Patel
Earlier this year we announced the public preview of Face Check with Microsoft Entra Verified ID – a privacy-respecting facial matching feature for high-assurance identity verifications and the first premium capability of Microsoft Entra Verified ID. Today I’m excited to announce that Face Check with Microsoft Entra Verified ID is generally available. It is offered both by itself and as part of the Microsoft Entra Suite, a complete identity solution that delivers Zero Trust access by combining network access, identity protection, governance, and identity verification capabilities.
Unlocking high-assurance verifications at scale
There’s a growing risk of impersonation and account takeover. Bad actors use insecure credentials in 66% of attack paths. For example, impersonators may use a compromised password to fraudulently log in to a system. With advancements in generative AI, complex impersonation tactics such as deepfakes are growing as well. Many organizations regularly onboard new employees remotely and offer a remote help desk. Without strong identity verification, how can organizations know who is on the other side of these digital interactions? Impersonators can easily bypass common verification methods such as counting bicycles on a CAPTCHA or asking which street you grew up on. As fraud skyrockets for businesses and consumers, and impersonation tactics have become increasingly complex, identity verification has never been more important.
Microsoft Entra Verified ID is based on open standards, enabling organizations to verify the widest variety of credentials using a simple API. Verified ID integrates with some of the leading verification partners to verify identity attributes for individuals (for example, a driver’s license and a liveness match) across 192 countries. Today, hundreds of organizations rely on Verified ID to remotely onboard new users and reduce fraud when providing self-service recovery. For example, using Verified ID, Skype has reduced fraudulent cases of registering Skype Phone Numbers in Japan by 90%.
Read the full post here: Face Check is now generally available
By Ankur Patel
Earlier this year we announced the public preview of Face Check with Microsoft Entra Verified ID – a privacy-respecting facial matching feature for high-assurance identity verifications and the first premium capability of Microsoft Entra Verified ID. Today I’m excited to announce that Face Check with Microsoft Entra Verified ID is generally available. It is offered both by itself and as part of the Microsoft Entra Suite, a complete identity solution that delivers Zero Trust access by combining network access, identity protection, governance, and identity verification capabilities.
Unlocking high-assurance verifications at scale
There’s a growing risk of impersonation and account takeover. Bad actors use insecure credentials in 66% of attack paths. For example, impersonators may use a compromised password to fraudulently log in to a system. With advancements in generative AI, complex impersonation tactics such as deepfakes are growing as well. Many organizations regularly onboard new employees remotely and offer a remote help desk. Without strong identity verification, how can organizations know who is on the other side of these digital interactions? Impersonators can easily bypass common verification methods such as counting bicycles on a CAPTCHA or asking which street you grew up on. As fraud skyrockets for businesses and consumers, and impersonation tactics have become increasingly complex, identity verification has never been more important.
Microsoft Entra Verified ID is based on open standards, enabling organizations to verify the widest variety of credentials using a simple API. Verified ID integrates with some of the leading verification partners to verify identity attributes for individuals (for example, a driver’s license and a liveness match) across 192 countries. Today, hundreds of organizations rely on Verified ID to remotely onboard new users and reduce fraud when providing self-service recovery. For example, using Verified ID, Skype has reduced fraudulent cases of registering Skype Phone Numbers in Japan by 90%.
Read the full post here: Face Check is now generally available
Work Smarter: Copilot Productivity Tips
A banner of the blog series title, “Work Smarter – Copilot Productivity Tips”
No matter if you’re an inbox-zero enthusiast or someone who lets emails pile up, one thing is certain: managing emails can be time-consuming and draining. Whether it’s personal or work-related, we all face the challenge of a busy inbox. Let Copilot in Outlook assist you in organizing your emails, enhancing your communication, and freeing up your time for what truly matters.
As part of a new weekly series that provides Copilot productivity tips, today our team at Microsoft will share with you three specific ways to use Copilot in Outlook. We launched this blog so you can start every week with more ways to save time at work.
Read along for Copilot tips in Outlook!
Tip 1: Organize my inbox
A quick way to tame that wild inbox is to create systems and categories. This not only allows me to tackle emails one category at a time but lets me prioritize which group I should respond to first.
In Outlook, navigate to the upper right corner and select the Copilot icon. From there will drop down four prompts, select the second one, “Organize my inbox.”
An image of suggested prompts from Copilot in Outlook when you open Copilot from the Copilot icon.
The prompt will now appear in the prompt box for you to fill in the details. For example, I want to make sure I catch up with anything that came in directly from my manager. I could ask Copilot to “Create an inbox rule to categorize all emails from Angela Byers as blue.”
An image of the prompt in Copilot for Outlook.
From there, Outlook will bring up the rules box to confirm your directive.
I used to find creating rules a bit of a chore but that has since changed. I now have different rules to categorize emails by subject and by sender, and it’s helped me ensure I never miss an important email. The color coding is also visually *chef’s kiss*. Rinse and repeat for that email inbox of your dreams!
Tip 2: Catch up
Have one of those days where your email is busting at the seams? Use this productivity tip to get a summary of your emails from Copilot. Navigate to the upper right corner and click the Copilot icon.
Once the Copilot chat has opened, key in this prompt: “Catch me up on emails from the past day. Organize and summarize by topic.”
(I can’t show you a screenshot of my inbox or the results, but just give it a try and let me know in the comments what you think).
Tip 3: Draft with Copilot
Now that your inbox is color coded and you’re received a download of your recent messages, it’s time to save some time actually drafting emails. Copilot helps me get more efficient by taking what I hope to convey in the prompt and writing a first draft for me.
In Outlook, you can start a new email (either a fresh email or hitting reply to an existing thread) navigate to the middle of the menu ribbon and select the Copilot logo, from there a drop–down menu will appear, select Draft with Copilot.
An image of Draft with Copilot option in the Copilot dropdown menu on the toolbar of a new email
Having Copilot work out a first draft saves me an underrated amount of time. I find decision-making much quicker when I have something to react to than when I have to draft something myself.
We hope you can apply these tips throughout the week to tame your Outlook inbox! Stay tuned for more productivity tips next Monday to learn additional ways to unlock more value with Copilot for Microsoft 365!
Microsoft Tech Community – Latest Blogs –Read More
Now Available: the Copilot for Microsoft 365 Risk Assessment QuickStart Guide
Copilot for Microsoft 365 is an intelligent assistant designed to enhance user productivity by leveraging relevant information and insights from various sources such as SharePoint, OneDrive, Outlook, Teams, Bing, and third-party solutions via connectors and extensions. Using natural language processing and machine learning, Copilot understands user queries and delivers personalized results, generating summaries, insights, and recommendations.
This QuickStart guide aims to assist organizations in performing a comprehensive risk assessment of Copilot for Microsoft 365. The document serves as an initial reference for risk identification, mitigation exploration, and stakeholder discussions. It is structured to cover:
AI Risks and Mitigations Framework: Outlining the primary categories of AI risks and how Microsoft addresses them at both company and service levels.
Sample Risk Assessment: Presenting a set of real customer-derived questions and answers to assess the service and its risk posture.
Additional Resources: Providing links to further materials on Copilot for Microsoft 365 and AI risk management.
Copilot for Microsoft 365 Risks and Mitigations
Bias
AI technologies can unintentionally perpetuate societal biases. Copilot for Microsoft 365 uses foundation models from OpenAI, which incorporate bias mitigation strategies during their training phases. Microsoft builds upon these mitigations by designing AI systems to provide equitable service quality across demographic groups, implementing measures to minimize disparities in outcomes for marginalized groups, and developing AI systems that avoid stereotyping or demeaning any cultural or societal group.
Disinformation
Disinformation is false information spread to deceive. This QuickStart guide covers Copilot for Microsoft 365 mitigations which include grounding responses in customer data and web data and requiring explicit user instruction for any action.
Overreliance and Automation Bias
Automation bias occurs when users over-rely on AI-generated information, potentially leading to misinformation. The QuickStart guide discusses methods of mitigating automation bias through measures such as informing users they are interacting with AI, disclaimers about the fallibility of AI, and more.
Ungroundedness (Hallucination)
AI models sometimes generate information not based on input data or grounding data. The QuickStart guide explores various mitigations for ungroundedness, including performance and effectiveness measures, metaprompt engineering, harms monitoring, and more.
Privacy
Data is a critical element for the functionality of an AI system, and without proper safeguards, this data may be exposed to risks. The QuickStart guide talks about how Microsoft ensures customer data remains private and is governed by stringent privacy commitments. Access controls and data usage parameters are also discussed.
Resiliency
Service disruptions can impact organizations. The QuickStart guide discusses mitigations such as redundancy, data integrity checking, uptime SLAs, and more.
Data Leakage
The QuickStart guide explores data leakage prevention (DLP) measures including zero trust, logical isolation, and rigorous encryption.
Security Vulnerabilities
Security is integral to AI development. Microsoft follows Security Development Lifecycle (SDL) practices, which include training, threat modelling, static and dynamic security testing, incident response, and more.
Sample Risk Assessment: Questions & Answers
This section contains a comprehensive set of questions and answers based on real customer inquiries. These cover privacy, security, supplier relationships, and model development concerns. The responses are informed by various Microsoft teams and direct attestations from OpenAI. Some key questions include:
Privacy: How personal data is anonymized before model training.
Security: Measures in place to prevent AI model compromise.
Supplier Relationships: Due diligence resources on OpenAI, a Microsoft strategic partner.
Model Development: Controls for data integrity, access management, and threat modeling.
By utilizing this guide, organizations can better understand the AI risk landscape integral to understanding Copilot for Microsoft 365 in an efficient manner enabling enterprise deployment. It serves as a foundational tool for risk assessment and frames further dialogue with Microsoft to address specific concerns or requirements.
Additional Resources
In addition to the framework and the sample assessment, the QuickStart guide provides links to a host of resources and materials that offer further detailed insights into Copilot for Microsoft 365 and AI risk management.
Microsoft Tech Community – Latest Blogs –Read More
Face Check is now generally available
Earlier this year we announced the public preview of Face Check with Microsoft Entra Verified ID – a privacy-respecting facial matching feature for high-assurance identity verifications and the first premium capability of Microsoft Entra Verified ID. Today I’m excited to announce that Face Check with Microsoft Entra Verified ID is generally available. It is offered both by itself and as part of the Microsoft Entra Suite, a complete identity solution that delivers Zero Trust access by combining network access, identity protection, governance, and identity verification capabilities.
Unlocking high-assurance verifications at scale
There’s a growing risk of impersonation and account takeover. Bad actors use insecure credentials in 66% of attack paths. For example, impersonators may use a compromised password to fraudulently log in to a system. With advancements in generative AI, complex impersonation tactics such as deepfakes are growing as well. Many organizations regularly onboard new employees remotely and offer a remote help desk. Without strong identity verification, how can organizations know who is on the other side of these digital interactions? Impersonators can easily bypass common verification methods such as counting bicycles on a CAPTCHA or asking which street you grew up on. As fraud skyrockets for businesses and consumers, and impersonation tactics have become increasingly complex, identity verification has never been more important.
Microsoft Entra Verified ID is based on open standards, enabling organizations to verify the widest variety of credentials using a simple API. Verified ID integrates with some of the leading verification partners to verify identity attributes for individuals (for example, a driver’s license and a liveness match) across 192 countries. Today, hundreds of organizations rely on Verified ID to remotely onboard new users and reduce fraud when providing self-service recovery. For example, using Verified ID, Skype has reduced fraudulent cases of registering Skype Phone Numbers in Japan by 90%.
Face Check with Microsoft Entra Verified ID
Powered by Azure AI services, Face Check adds a critical layer of trust by matching a user’s real-time selfie and the photo on their Verified ID, which is usually from a trusted source such as a passport or driver’s license. By sharing only match results and not any sensitive identity data, Face Check strengthens an organization’s identity verification while protecting user privacy. It can detect and reject various spoofing techniques, including deepfakes, to fully protect your users’ identities.
BEMO, a security solution provider for SMBs, integrated Face Check into its help desk to increase verification accuracy, reduce verification time, and lower costs. The company used Face Check with Microsoft Entra Verified ID to protect its most sensitive accounts which belong to C-level executives and IT administrators.
Face Check not only helps BEMO improve customer security and strengthen user data privacy, but it also created a 90% efficiency improvement in addressing customer issues. BEMO’s help desk now completes a manual identity verification in 30 minutes, down from 5.5 hours before implementing Face Check.
“Security is always great when you apply it in layers, and this verification is an additional layer that we’ll be able to provide to our customers. It’s one more way we can help them feel secure.” – Jose Castelan, Support and Managed Services Team Lead, BEMO
Check out the video below to learn more about how your organization can use Face Check with Microsoft Entra Verified ID:
Jumpstart with partners
Our partners specialize in implementing Face Check with Microsoft Entra Verified ID in specific use cases or verifying certain identity attributes such as employment status, education, or government-issued IDs (with partners like LexisNexis® Risk Solutions, Au10tix, and IDEMIA). These partners extend Verified ID’s capabilities to provide a variety of verification solutions that will work for your business’s specific needs.
Explore our partner gallery to learn more about our partners and how they can help you get started with Verified ID.
Start using Face Check with Microsoft Entra Verified ID
Face Check is a premium feature of Verified ID. After you set up your Verified ID tenant, there are two purchase options to enable Face Check and start verifying:
1. Begin the Entra Suite free trial, which includes 8 Face Check verifications per user per month.
2. Enable Face Check within Verified ID and pay $0.25 per verification.
Visit the Microsoft Entra pricing page for more details.
What’s Next?
Learn more about how Microsoft Entra Verified ID works and how organizations are using it today, and join us for the Microsoft Entra Suite Tech Accelerator on August 14 to learn about the latest identity management and end-to-end security innovations.
Ankur Patel, Head of Product for Microsoft Entra Verified ID
Read more on this topic
Watch the Zero Trust spotlight
Learn about the Microsoft Entra Suite
Learn more about Face Check with Microsoft Entra Verified ID in the FAQ
Learn more about Microsoft Entra
Prevent identity attacks, ensure least privilege access, unify access controls, and improve the experience for users with comprehensive identity and network access solutions across on-premises and clouds.
Microsoft Entra News and Insights | Microsoft Security Blog
Microsoft Entra blog | Tech Community
Microsoft Entra documentation | Microsoft Learn
Microsoft Tech Community – Latest Blogs –Read More
eDiscovery launches a modern, intuitive user experience
This month, we have launched a redesigned Microsoft Purview eDiscovery product experience in public preview. This improved user experience revolutionizes your data search, review and export tasks within eDiscovery. Our new user-friendly and feature-rich eDiscovery experience is not just about finding and preserving data, it’s about doing it with unprecedented efficiency and ease. The modern user experience of eDiscovery addresses some long-standing customer requests, such as enhanced search capabilities with MessageID, Sensitive Information Types (SITs) and sensitivity labels. It also introduces innovative features like draft query with Copilot and search using audit log. These changes, driven by customer feedback and our commitment to innovation, offer tangible value by saving time and reducing costs in the eDiscovery process.
The new eDiscovery experience is exclusively available in the Microsoft Purview portal. The new Microsoft Purview portal is a unified platform that streamlines data governance, data security, and data compliance across your entire data estate. It offers a more intuitive experience, allowing users to easily navigate and manage their compliance needs.
Unified experience
One of the benefits of the new improved eDiscovery offers a unified, consistent, and intuitive experience across different licensing tiers. Whether your license includes eDiscovery standard or premium, you can use the same workflow to create cases, conduct searches, apply holds, and export data. This simplifies the training and education process for organizations that upgrade their license and want to access premium eDiscovery features. Unlike the previous experience, where Content Search, eDiscovery (Standard), and eDiscovery (Premium) had different workflows and behaviors, the new experience lets you access eDiscovery capabilities seamlessly regardless of your license level. E5 license holders have the option to use premium features such as exporting cloud attachments and Teams conversation threading at the appropriate steps in the workflow. Moreover, users still have access to all existing Content Searches and both Standard and Premium eDiscovery cases on the unified eDiscovery case list page in the Microsoft Purview portal.
The new experience also strengthens the security controls for Content Search by placing them in an eDiscovery case. This allows eDiscovery administrators to control who can access and use existing Content Searches and generated exports. Administrators can add or remove users from the Content Search case as needed. This way, they can prevent unauthorized access to sensitive search data and stop Content Search when it is no longer required. Moreover, this helps maintain the integrity and confidentiality of the investigation process. The new security controls ensure that only authorized personnel can access sensitive data, reducing the risk of data breaches and complying with legal and regulatory standards.
Enhanced data source management
Efficient litigation and investigation workflows hinge on the ability to precisely select data sources and locations in the eDiscovery process. This enables legal teams to swiftly preserve relevant information and minimize the risk of missing critical evidence. The improved data source picking capability allows for a more targeted and effective search, which is essential in responding to legal matters or internal investigations. It enables users to apply holds and conduct searches with greater accuracy, ensuring that all pertinent information is captured without unnecessary data proliferation. This improvement not only enhances the quality of the review, but also reduces the overall costs associated with data storage and management.
The new eDiscovery experience makes data source location mapping and management better as well. You can now perform a user or group search with different identifiers and see their data hierarchy tree, including their mailbox and OneDrive. For example, eDiscovery users can use any of following identifiers: Name, user principal name (UPN), SMTP address, or OneDrive URL. The data source picker streamlines the eDiscovery workflow by displaying all potential matches and their locations, along with related sources such as frequent collaborators, group memberships, and direct reports. This allows for the addition of these sources to search or hold scope without relying on external teams for information on collaboration patterns, Teams/Group memberships, or organizational hierarchies.
Figure 1: New data source view with ability to associate person’s mailbox and OneDrive, exploring to a person’s frequent collaborator and ability to query data source updates.
The “sync” capability in the new data source management flow is a significant addition that ensures eDiscovery users are always informed about the latest changes in data locations. With this feature, users can now query whether a specific data source has newly provisioned data locations or if any have been removed. For example, if a private channel is created for a Teams group, this feature alerts eDiscovery users to the new site’s existence, allowing them to quickly and easily include it in their search scope, ensuring no new data slips through the cracks. This real-time update capability empowers users to make informed decisions about including or excluding additional data locations in their investigations. This capability ensures that their eDiscovery process remains accurate and up-to-date with the latest data landscape changes. It is a proactive approach to data management that enhances the efficiency and effectiveness of eDiscovery operations, providing users with the agility to adapt to changes swiftly.
Improved integration with Microsoft Information Protection
The new eDiscovery experience now supports querying by Sensitive Information Types (SITs) and sensitivity labels. Labeling, classifying, and encrypting your organization’s data is a best practice that serves multiple essential purposes. It helps to ensure that sensitive information is handled appropriately, reducing the risk of unauthorized access and data breaches. By classifying data, organizations can apply the right level of protection to different types of information, which is crucial for compliance with various regulations and standards. Moreover, encryption adds a layer of security that keeps data safe even if it falls into the wrong hands. It ensures that only authorized users can access and read the information, protecting it from external threats and internal leaks.
The new eDiscovery search functionality supports searches for emails and documents classified by SITs or specific sensitivity labels, facilitating the collection and review of data aligned with its classification for thorough investigations. This capability compresses the volume of evidence required for review, significantly reducing both the time and cost of the process. The support of efficient document location and management by targeting specific sensitivity labels unlocks the ability for organizations to validate and understand how sensitivity labels are utilized. This is exemplified by the ability to conduct collections across locations or the entire tenant for a particular label, using the review set to assess label application. Additionally, combining this with SIT searches helps verify correct data classification. For example, it ensures that all credit card data is appropriately labeled as highly confidential by reviewing items containing credit card data that are not marked as such, thereby streamlining compliance and adherence to security policies.
Figure 2: Better integration with Microsoft Information Protection means the ability to search labeled and protected data by SIT and sensitivity label.Figure 3: Better integration with Microsoft Information Protection means the ability to search labeled and protected data by SIT and sensitivity label.
Enhanced investigation capabilities
The new eDiscovery experience introduces a powerful capability to expedite security investigations, particularly in scenarios involving a potentially compromised account. By leveraging the ability to search by audit log, investigators can swiftly assess the account’s activities, pinpointing impacted files. As part of the investigative feature, eDiscovery search can also make use of evidence file as search input. It enables a rapid analysis of file content patterns or signatures. This feature is crucial for identifying similar or related content, providing a streamlined approach to discover if sensitive files have been copied or moved, thereby enhancing the efficiency and effectiveness of the security response.
The enhanced search capability by identifier in the new eDiscovery UX is a game-changer for customers, offering a direct route to the exact message or file needed. With the ability to search using a messageID for mailbox items or a path for SharePoint items, users can quickly locate and retrieve the specific item they require. This precision not only streamlines evidence collection but also accelerates the process of purging leaked data for spillage cleanup. It’s a significant time-saver that simplifies the workflow, allowing customers to focus on what matters most – securing and managing their digital environment efficiently, while targeting relevant data.
Building on the data spillage scenario, our search and purge tool for mailbox items, including Teams messages, also received a significant 10x enhancement. Where previously administrators could only purge 10 items per mailbox location, they can now purge up to 100 items per mailbox location. This enhancement is a benefit for administrators tasked with responding to data spills or needing to remediate data within Teams or Exchange, allowing for a more comprehensive and efficient purge process. With all these investigative capability updates, now the security operations team is ready to embrace the expanded functionality and take their eDiscovery operations to the next level.
Microsoft Security Copilot capabilities
The recently released Microsoft Security Copilot’s capabilities in eDiscovery are transformative, particularly in generating KeyQL from natural language and providing contextual summarization and answering abilities in review sets. These features significantly lower the learning curve for KeyQL, enabling users to construct complex queries with ease. Instead of mastering the intricacies of KeyQL, users can simply describe what they are looking for using natural language, and Copilot translates that into a precise KeyQL statement. This not only saves time but also makes the power of eDiscovery accessible to a broader range of users, regardless of their technical expertise.
Figure 4: Draft query faster with Copilot’s N2KeyQL capability.
Moreover, Copilot’s summarization skills streamline the review process by distilling key insights from extensive datasets. Users can quickly grasp the essence of large volumes of data, which accelerates the review process and aids in identifying the most pertinent information. This is particularly beneficial in legal and compliance contexts, where time is often of the essence, and the ability to rapidly process and understand information can have significant implications.
Figure 5: Copilot summarization skill in Review Set helps reviewer review content by assessing summary of the item – even when the conversation is in not in English.
Additional export options
The new eDiscovery experience introduces a highly anticipated suite of export setting enhancements. The contextual conversation setting is now distinct from the conversation transcript setting, offering greater flexibility in how Teams conversations are exported. The ability to export into a single PST allows for the consolidation of files/items from multiple locations, simplifying the post-export workflow. Export can now give friendly names to each item, eliminating the need for users to decipher item GUIDs, and making identification straightforward. Truncation in export addresses the challenges of zip file path character limits. Additionally, the expanded versioning options empower users to include all versions or select the latest 10 or 100, providing tailored control over the data. These improvements not only meet user expectations but also significantly benefit customers by streamlining the eDiscovery process and enhancing overall efficiency.
Additional enhancements
As part of the new experience, we are introducing the review set query report, which generates a hit-by-term report based on a KQL query. This query report allows users to quickly see the count and volume of items hit on a particular keyword or a list of compound queries, and can be optionally downloaded. By providing a detailed breakdown of where and how often each term appears, it streamlines the review by focusing on the most relevant documents, reducing the volume of data that needs to be manually reviewed, and offers a better understanding of which terms may be too broad or too narrow.
As part of the improved user experience, all long-running processes now show a transparent and informative progress bar. This progress bar provides users with real-time visibility into the status of their searches and exports, allowing eDiscovery practitioners to better plan their workflow and manage their time effectively. This feature is particularly beneficial in the context of legal investigations, where timing is often critical, and users need to anticipate when they can proceed to the next steps. This level of process transparency allows users to stay informed and make decisions accordingly.
Figure 6: Transparent progress bar for all long-running processes detailing scope of the process and estimated time to complete.
In addition to progress transparency, all processes in the new eDiscovery experience will include a full report detailing the information related to completed processes. The defensibility of eDiscovery cases and investigations is paramount. The full reporting capabilities for processes such as exports, searches, and holds provide critical transparency. For example, it allows for a comprehensive audit of what was searched or exported, the specific timing, and the settings used. For customers, this means a significant increase in trust and defensibility of the eDiscovery process. This enhancement not only bolsters the integrity of the eDiscovery process but also reinforces the commitment to delivering customer-centric solutions that meet the rigorous demands of legal compliance and data management.
Hold policy detail view also received an upgrade as part of this new eDiscovery release. Customers now can access the hold policy view with detailed information on all locations and their respective hold status. This detailed view is instrumental in providing a transparent audit of what location is on hold, ensuring that all relevant data is preserved, and that no inadvertent destruction of evidence occurs during the process. Customers can download and analyze the full detailed hold location report, ensuring that all necessary content is accounted for and that legal obligations are met.
As we conclude this exploration of the modernized Microsoft Purview eDiscovery (preview) experience, it’s clear that the transformative enhancements are set to redefine the landscape of legal compliance and security investigations. The new experience, with its intuitive design and comprehensive set of new capabilities, streamlines the eDiscovery process, making it more efficient and accessible than ever before. The new eDiscovery experience is currently in public preview and is expected to be Generally Available by the end of 2024.
Thank you for joining us on this journey through the latest advancements in eDiscovery. We are excited to see how these changes will empower legal and compliance teams to achieve new levels of efficiency and effectiveness in their important work. To learn more about the changes in eDiscovery, visit our product documentation. As always, we are eager to hear your feedback and continue innovating to improve your experience. We welcome your thoughts via the Microsoft Purview portal’s feedback button.
We hope these enhancements improve your day-to-day experience and ultimately streamline the eDiscovery process, making it more efficient and accessible than ever before. The new eDiscovery experience is currently in public preview and is expected to be Generally Available by the end of 2024.
Learn more
We are excited to see how these changes will empower legal and compliance teams to achieve new levels of efficiency and effectiveness in their important work. Check out our interactive guide at https://aka.ms/eDiscoverynewUX to better understand the changes in eDiscovery. As always, we are eager to hear your feedback and continue innovating to improve your experience. We welcome your thoughts via the Microsoft Purview portal’s feedback button.
To learn more about eDiscovery, visit our Microsoft documentation at http://aka.ms/eDiscoveryPremium, or our “Become an eDiscovery Ninja” page at https://aka.ms/ediscoveryninja. If you have yet to try Microsoft Purview solutions, we are happy to share that there is an easy way for eligible customers to begin a free trial within the Microsoft Purview compliance portal. By enabling the trial in the compliance portal, you can quickly start using all capabilities of Microsoft Purview, including Insider Risk Management, Records Management, Audit, eDiscovery, Communication Compliance, Information Protection, Data Lifecycle Management, Data Loss Prevention, and Compliance Manager.
Microsoft Tech Community – Latest Blogs –Read More
Has anyone found a way to enable/disable tabs in App Designer?
I am building a fairly simple GUI in I have tried implementations using findjobj, such as:
jtabgroup=findjobj(tabgrp);
jtabgroup.setEnabledAt(1,0);
but I am unsure if the app class can be read the same way (I’m very new to Java implementations). Any help would be much appreciated!I am building a fairly simple GUI in I have tried implementations using findjobj, such as:
jtabgroup=findjobj(tabgrp);
jtabgroup.setEnabledAt(1,0);
but I am unsure if the app class can be read the same way (I’m very new to Java implementations). Any help would be much appreciated! I am building a fairly simple GUI in I have tried implementations using findjobj, such as:
jtabgroup=findjobj(tabgrp);
jtabgroup.setEnabledAt(1,0);
but I am unsure if the app class can be read the same way (I’m very new to Java implementations). Any help would be much appreciated! gui, app designer MATLAB Answers — New Questions
Why do my laptop and work computer produce different results when adding a small term to a matrix?
In the following code, when I add the term eye(8)*(0.0001) to the matrix F, my laptop and my work computer return different values for the variable Uk. Could anyone please help me understand what is happening?
I am using MATLAB R2024a on both computers.
clear; clc;
% constants
a = 1;
x1 = 1;
x2 = 0;
x4 = 0;
lambda = 1;
sigma3 = diag([1 1 1 1 -1 -1 -1 -1]);
kx = -2.0944;
ky = -3.6276;
F = [ – 0.4444*x4*lambda^3 + (2.0*x1 + 2.0*x2)*lambda, – lambda*(x2*(exp(-a*ky*0.866i) + exp(a*ky*0.866i)) + x1*(exp(-a*kx*0.5i) + exp(a*kx*0.5i)))*(0.3333 – 4.082e-17i) – 1.0*x4*lambda^3*(exp(-a*kx*0.5i)*(0.8889 + 5.551e-17i) + exp(a*kx*0.5i)*(0.8889 + 5.551e-17i)) + x4*lambda^3*(exp(-a*kx*0.5i)*(0.8889 + 1.11e-16i) + exp(a*kx*0.5i)*(0.8889 + 1.11e-16i)) + x4*lambda^3*(exp(-a*kx*0.5i)*(0.2222 – 2.721e-17i) + exp(a*kx*0.5i)*(0.2222 – 2.721e-17i)), lambda*(x1*(exp(- a*kx*0.25i + a*ky*0.433i) + exp(a*kx*0.25i – a*ky*0.433i)) + x2*(exp(- a*kx*0.75i – a*ky*0.433i) + exp(a*kx*0.75i + a*ky*0.433i)))*(0.1667 – 0.2887i) – x4*lambda^3*(exp(- a*kx*0.25i + a*ky*0.433i) + exp(a*kx*0.25i – a*ky*0.433i))*(0.7778 – 1.347i), lambda*(x1*(exp(- a*kx*0.25i – a*ky*0.433i) + exp(a*kx*0.25i + a*ky*0.433i)) + x2*(exp(- a*kx*0.75i + a*ky*0.433i) + exp(a*kx*0.75i – a*ky*0.433i)))*(0.1667 + 0.2887i) – x4*lambda^3*(exp(- a*kx*0.25i – a*ky*0.433i) + exp(a*kx*0.25i + a*ky*0.433i))*(0.1111 + 0.1925i), 0, lambda*(x2*(exp(-a*ky*0.866i) + exp(a*ky*0.866i)) + x1*(exp(-a*kx*0.5i) + exp(a*kx*0.5i)))*(0.6667 – 8.164e-17i) + x4*lambda^3*(exp(-a*kx*0.5i) + exp(a*kx*0.5i))*1.54i – 1.0*x4*lambda^3*(exp(-a*kx*0.5i)*(0.4444 – 5.443e-17i) + exp(a*kx*0.5i)*(0.4444 – 5.443e-17i)), – lambda*(x1*(exp(- a*kx*0.25i + a*ky*0.433i) + exp(a*kx*0.25i – a*ky*0.433i)) + x2*(exp(- a*kx*0.75i – a*ky*0.433i) + exp(a*kx*0.75i + a*ky*0.433i)))*(0.3333 – 0.5774i) – x4*lambda^3*(exp(- a*kx*0.25i + a*ky*0.433i) + exp(a*kx*0.25i – a*ky*0.433i))*(0.6667 + 0.3849i) + x4*lambda^3*(exp(- a*kx*0.25i + a*ky*0.433i)*(0.8889 + 1.11e-16i) + exp(a*kx*0.25i – a*ky*0.433i)*(0.8889 + 1.11e-16i)), – lambda*(x1*(exp(- a*kx*0.25i – a*ky*0.433i) + exp(a*kx*0.25i + a*ky*0.433i)) + x2*(exp(- a*kx*0.75i + a*ky*0.433i) + exp(a*kx*0.75i – a*ky*0.433i)))*(0.3333 + 0.5774i) – x4*lambda^3*(exp(- a*kx*0.25i – a*ky*0.433i) + exp(a*kx*0.25i + a*ky*0.433i))*(0.2222 – 1.155i) – 1.0*x4*lambda^3*(exp(- a*kx*0.25i – a*ky*0.433i)*(0.8889 + 5.551e-17i) + exp(a*kx*0.25i + a*ky*0.433i)*(0.8889 + 5.551e-17i))
– lambda*(x2*(exp(-a*ky*0.866i) + exp(a*ky*0.866i)) + x1*(exp(-a*kx*0.5i) + exp(a*kx*0.5i)))*(0.3333 + 4.082e-17i) – 1.0*x4*lambda^3*(exp(-a*kx*0.5i)*(0.8889 – 5.551e-17i) + exp(a*kx*0.5i)*(0.8889 – 5.551e-17i)) + x4*lambda^3*(exp(-a*kx*0.5i)*(0.8889 – 1.11e-16i) + exp(a*kx*0.5i)*(0.8889 – 1.11e-16i)) + x4*lambda^3*(exp(-a*kx*0.5i)*(0.2222 + 2.721e-17i) + exp(a*kx*0.5i)*(0.2222 + 2.721e-17i)), – 0.4444*x4*lambda^3 + (2.0*x1 + 2.0*x2)*lambda, lambda*(x1*(exp(- a*kx*0.25i – a*ky*0.433i) + exp(a*kx*0.25i + a*ky*0.433i)) + x2*(exp(- a*kx*0.75i + a*ky*0.433i) + exp(a*kx*0.75i – a*ky*0.433i)))*(0.1667 – 0.2887i) – x4*lambda^3*(exp(- a*kx*0.25i – a*ky*0.433i) + exp(a*kx*0.25i + a*ky*0.433i))*(0.1111 – 0.1925i), lambda*(x1*(exp(- a*kx*0.25i + a*ky*0.433i) + exp(a*kx*0.25i – a*ky*0.433i)) + x2*(exp(- a*kx*0.75i – a*ky*0.433i) + exp(a*kx*0.75i + a*ky*0.433i)))*(0.1667 + 0.2887i) – x4*lambda^3*(exp(- a*kx*0.25i + a*ky*0.433i) + exp(a*kx*0.25i – a*ky*0.433i))*(0.7778 + 1.347i), lambda*(x2*(exp(-a*ky*0.866i) + exp(a*ky*0.866i)) + x1*(exp(-a*kx*0.5i) + exp(a*kx*0.5i)))*(0.6667 – 8.164e-17i) + x4*lambda^3*(exp(-a*kx*0.5i) + exp(a*kx*0.5i))*1.54i – 1.0*x4*lambda^3*(exp(-a*kx*0.5i)*(0.4444 – 5.443e-17i) + exp(a*kx*0.5i)*(0.4444 – 5.443e-17i)), 0, lambda*(x1*(exp(- a*kx*0.25i – a*ky*0.433i) + exp(a*kx*0.25i + a*ky*0.433i)) + x2*(exp(- a*kx*0.75i + a*ky*0.433i) + exp(a*kx*0.75i – a*ky*0.433i)))*(0.6667 – 1.027e-33i) – x4*lambda^3*(exp(- a*kx*0.25i – a*ky*0.433i) + exp(a*kx*0.25i + a*ky*0.433i))*1.54i – 1.0*x4*lambda^3*(0.4444*exp(- a*kx*0.25i – a*ky*0.433i) + 0.4444*exp(a*kx*0.25i + a*ky*0.433i)), lambda*(x1*(exp(- a*kx*0.25i + a*ky*0.433i) + exp(a*kx*0.25i – a*ky*0.433i)) + x2*(exp(- a*kx*0.75i – a*ky*0.433i) + exp(a*kx*0.75i + a*ky*0.433i)))*(0.6667 – 1.027e-33i) – 0.8889*x4*lambda^3*(exp(- a*kx*0.25i + a*ky*0.433i) + exp(a*kx*0.25i – a*ky*0.433i)) + x4*lambda^3*(0.4444*exp(- a*kx*0.25i + a*ky*0.433i) + 0.4444*exp(a*kx*0.25i – a*ky*0.433i))
lambda*(x1*(exp(- a*kx*0.25i + a*ky*0.433i) + exp(a*kx*0.25i – a*ky*0.433i)) + x2*(exp(- a*kx*0.75i – a*ky*0.433i) + exp(a*kx*0.75i + a*ky*0.433i)))*(0.1667 + 0.2887i) – x4*lambda^3*(exp(- a*kx*0.25i + a*ky*0.433i) + exp(a*kx*0.25i – a*ky*0.433i))*(0.7778 + 1.347i), lambda*(x1*(exp(- a*kx*0.25i – a*ky*0.433i) + exp(a*kx*0.25i + a*ky*0.433i)) + x2*(exp(- a*kx*0.75i + a*ky*0.433i) + exp(a*kx*0.75i – a*ky*0.433i)))*(0.1667 + 0.2887i) – x4*lambda^3*(exp(- a*kx*0.25i – a*ky*0.433i) + exp(a*kx*0.25i + a*ky*0.433i))*(0.1111 + 0.1925i), – 0.4444*x4*lambda^3 + (2.0*x1 + 2.0*x2)*lambda, lambda*(x2*(exp(-a*ky*0.866i) + exp(a*ky*0.866i)) + x1*(exp(-a*kx*0.5i) + exp(a*kx*0.5i)))*(0.1667 – 0.2887i) – x4*lambda^3*(exp(-a*kx*0.5i) + exp(a*kx*0.5i))*(0.1111 – 0.1925i), – lambda*(x1*(exp(- a*kx*0.25i + a*ky*0.433i) + exp(a*kx*0.25i – a*ky*0.433i)) + x2*(exp(- a*kx*0.75i – a*ky*0.433i) + exp(a*kx*0.75i + a*ky*0.433i)))*(0.3333 – 0.5774i) – x4*lambda^3*(exp(- a*kx*0.25i + a*ky*0.433i) + exp(a*kx*0.25i – a*ky*0.433i))*(0.6667 + 0.3849i) + x4*lambda^3*(exp(- a*kx*0.25i + a*ky*0.433i)*(0.8889 + 1.11e-16i) + exp(a*kx*0.25i – a*ky*0.433i)*(0.8889 + 1.11e-16i)), lambda*(x1*(exp(- a*kx*0.25i – a*ky*0.433i) + exp(a*kx*0.25i + a*ky*0.433i)) + x2*(exp(- a*kx*0.75i + a*ky*0.433i) + exp(a*kx*0.75i – a*ky*0.433i)))*(0.6667 – 1.027e-33i) – x4*lambda^3*(exp(- a*kx*0.25i – a*ky*0.433i) + exp(a*kx*0.25i + a*ky*0.433i))*(1.257 + 1.54i), 0, 0.6667*lambda*(x2*(exp(-a*ky*0.866i) + exp(a*ky*0.866i)) + x1*(exp(-a*kx*0.5i) + exp(a*kx*0.5i))) + x4*lambda^3*(exp(-a*kx*0.5i) + exp(a*kx*0.5i))*1.54i – 1.0*x4*lambda^3*(exp(-a*kx*0.5i)*(0.4444 – 1.388e-17i) + exp(a*kx*0.5i)*(0.4444 – 1.388e-17i))
lambda*(x1*(exp(- a*kx*0.25i – a*ky*0.433i) + exp(a*kx*0.25i + a*ky*0.433i)) + x2*(exp(- a*kx*0.75i + a*ky*0.433i) + exp(a*kx*0.75i – a*ky*0.433i)))*(0.1667 – 0.2887i) – x4*lambda^3*(exp(- a*kx*0.25i – a*ky*0.433i) + exp(a*kx*0.25i + a*ky*0.433i))*(0.1111 – 0.1925i), lambda*(x1*(exp(- a*kx*0.25i + a*ky*0.433i) + exp(a*kx*0.25i – a*ky*0.433i)) + x2*(exp(- a*kx*0.75i – a*ky*0.433i) + exp(a*kx*0.75i + a*ky*0.433i)))*(0.1667 – 0.2887i) – x4*lambda^3*(exp(- a*kx*0.25i + a*ky*0.433i) + exp(a*kx*0.25i – a*ky*0.433i))*(0.7778 – 1.347i), lambda*(x2*(exp(-a*ky*0.866i) + exp(a*ky*0.866i)) + x1*(exp(-a*kx*0.5i) + exp(a*kx*0.5i)))*(0.1667 + 0.2887i) – x4*lambda^3*(exp(-a*kx*0.5i) + exp(a*kx*0.5i))*(0.1111 + 0.1925i), – 0.4444*x4*lambda^3 + (2.0*x1 + 2.0*x2)*lambda, – lambda*(x1*(exp(- a*kx*0.25i – a*ky*0.433i) + exp(a*kx*0.25i + a*ky*0.433i)) + x2*(exp(- a*kx*0.75i + a*ky*0.433i) + exp(a*kx*0.75i – a*ky*0.433i)))*(0.3333 + 0.5774i) – x4*lambda^3*(exp(- a*kx*0.25i – a*ky*0.433i) + exp(a*kx*0.25i + a*ky*0.433i))*(0.2222 – 1.155i) – 1.0*x4*lambda^3*(exp(- a*kx*0.25i – a*ky*0.433i)*(0.8889 + 5.551e-17i) + exp(a*kx*0.25i + a*ky*0.433i)*(0.8889 + 5.551e-17i)), lambda*(x1*(exp(- a*kx*0.25i + a*ky*0.433i) + exp(a*kx*0.25i – a*ky*0.433i)) + x2*(exp(- a*kx*0.75i – a*ky*0.433i) + exp(a*kx*0.75i + a*ky*0.433i)))*(0.6667 – 1.027e-33i) – 0.8889*x4*lambda^3*(exp(- a*kx*0.25i + a*ky*0.433i) + exp(a*kx*0.25i – a*ky*0.433i)) + x4*lambda^3*(0.4444*exp(- a*kx*0.25i + a*ky*0.433i) + 0.4444*exp(a*kx*0.25i – a*ky*0.433i)), 0.6667*lambda*(x2*(exp(-a*ky*0.866i) + exp(a*ky*0.866i)) + x1*(exp(-a*kx*0.5i) + exp(a*kx*0.5i))) + x4*lambda^3*(exp(-a*kx*0.5i) + exp(a*kx*0.5i))*1.54i – 1.0*x4*lambda^3*(exp(-a*kx*0.5i)*(0.4444 – 1.388e-17i) + exp(a*kx*0.5i)*(0.4444 – 1.388e-17i)), 0
0, lambda*(x2*(exp(-a*ky*0.866i) + exp(a*ky*0.866i)) + x1*(exp(-a*kx*0.5i) + exp(a*kx*0.5i)))*(0.6667 + 8.164e-17i) – x4*lambda^3*(exp(-a*kx*0.5i) + exp(a*kx*0.5i))*1.54i – 1.0*x4*lambda^3*(exp(-a*kx*0.5i)*(0.4444 + 5.443e-17i) + exp(a*kx*0.5i)*(0.4444 + 5.443e-17i)), – lambda*(x1*(exp(- a*kx*0.25i + a*ky*0.433i) + exp(a*kx*0.25i – a*ky*0.433i)) + x2*(exp(- a*kx*0.75i – a*ky*0.433i) + exp(a*kx*0.75i + a*ky*0.433i)))*(0.3333 + 0.5774i) – x4*lambda^3*(exp(- a*kx*0.25i + a*ky*0.433i) + exp(a*kx*0.25i – a*ky*0.433i))*(0.6667 – 0.3849i) + x4*lambda^3*(exp(- a*kx*0.25i + a*ky*0.433i)*(0.8889 – 1.11e-16i) + exp(a*kx*0.25i – a*ky*0.433i)*(0.8889 – 1.11e-16i)), – lambda*(x1*(exp(- a*kx*0.25i – a*ky*0.433i) + exp(a*kx*0.25i + a*ky*0.433i)) + x2*(exp(- a*kx*0.75i + a*ky*0.433i) + exp(a*kx*0.75i – a*ky*0.433i)))*(0.3333 – 0.5774i) – x4*lambda^3*(exp(- a*kx*0.25i – a*ky*0.433i) + exp(a*kx*0.25i + a*ky*0.433i))*(0.2222 + 1.155i) – 1.0*x4*lambda^3*(exp(- a*kx*0.25i – a*ky*0.433i)*(0.8889 – 5.551e-17i) + exp(a*kx*0.25i + a*ky*0.433i)*(0.8889 – 5.551e-17i)), – 0.4444*x4*lambda^3 + (2.0*x1 + 2.0*x2)*lambda, – lambda*(x2*(exp(-a*ky*0.866i) + exp(a*ky*0.866i)) + x1*(exp(-a*kx*0.5i) + exp(a*kx*0.5i)))*(0.3333 + 4.082e-17i) – 1.0*x4*lambda^3*(exp(-a*kx*0.5i)*(0.8889 – 5.551e-17i) + exp(a*kx*0.5i)*(0.8889 – 5.551e-17i)) + x4*lambda^3*(exp(-a*kx*0.5i)*(0.8889 – 1.11e-16i) + exp(a*kx*0.5i)*(0.8889 – 1.11e-16i)) + x4*lambda^3*(exp(-a*kx*0.5i)*(0.2222 + 2.721e-17i) + exp(a*kx*0.5i)*(0.2222 + 2.721e-17i)), lambda*(x1*(exp(- a*kx*0.25i + a*ky*0.433i) + exp(a*kx*0.25i – a*ky*0.433i)) + x2*(exp(- a*kx*0.75i – a*ky*0.433i) + exp(a*kx*0.75i + a*ky*0.433i)))*(0.1667 + 0.2887i) – x4*lambda^3*(exp(- a*kx*0.25i + a*ky*0.433i) + exp(a*kx*0.25i – a*ky*0.433i))*(0.7778 + 1.347i), lambda*(x1*(exp(- a*kx*0.25i – a*ky*0.433i) + exp(a*kx*0.25i + a*ky*0.433i)) + x2*(exp(- a*kx*0.75i + a*ky*0.433i) + exp(a*kx*0.75i – a*ky*0.433i)))*(0.1667 – 0.2887i) – x4*lambda^3*(exp(- a*kx*0.25i – a*ky*0.433i) + exp(a*kx*0.25i + a*ky*0.433i))*(0.1111 – 0.1925i)
lambda*(x2*(exp(-a*ky*0.866i) + exp(a*ky*0.866i)) + x1*(exp(-a*kx*0.5i) + exp(a*kx*0.5i)))*(0.6667 + 8.164e-17i) – x4*lambda^3*(exp(-a*kx*0.5i) + exp(a*kx*0.5i))*1.54i – 1.0*x4*lambda^3*(exp(-a*kx*0.5i)*(0.4444 + 5.443e-17i) + exp(a*kx*0.5i)*(0.4444 + 5.443e-17i)), 0, lambda*(x1*(exp(- a*kx*0.25i – a*ky*0.433i) + exp(a*kx*0.25i + a*ky*0.433i)) + x2*(exp(- a*kx*0.75i + a*ky*0.433i) + exp(a*kx*0.75i – a*ky*0.433i)))*(0.6667 + 1.027e-33i) – x4*lambda^3*(exp(- a*kx*0.25i – a*ky*0.433i) + exp(a*kx*0.25i + a*ky*0.433i))*(1.257 – 1.54i), lambda*(x1*(exp(- a*kx*0.25i + a*ky*0.433i) + exp(a*kx*0.25i – a*ky*0.433i)) + x2*(exp(- a*kx*0.75i – a*ky*0.433i) + exp(a*kx*0.75i + a*ky*0.433i)))*(0.6667 + 1.027e-33i) – 0.8889*x4*lambda^3*(exp(- a*kx*0.25i + a*ky*0.433i) + exp(a*kx*0.25i – a*ky*0.433i)) + x4*lambda^3*(0.4444*exp(- a*kx*0.25i + a*ky*0.433i) + 0.4444*exp(a*kx*0.25i – a*ky*0.433i)), – lambda*(x2*(exp(-a*ky*0.866i) + exp(a*ky*0.866i)) + x1*(exp(-a*kx*0.5i) + exp(a*kx*0.5i)))*(0.3333 – 4.082e-17i) – 1.0*x4*lambda^3*(exp(-a*kx*0.5i)*(0.8889 + 5.551e-17i) + exp(a*kx*0.5i)*(0.8889 + 5.551e-17i)) + x4*lambda^3*(exp(-a*kx*0.5i)*(0.8889 + 1.11e-16i) + exp(a*kx*0.5i)*(0.8889 + 1.11e-16i)) + x4*lambda^3*(exp(-a*kx*0.5i)*(0.2222 – 2.721e-17i) + exp(a*kx*0.5i)*(0.2222 – 2.721e-17i)), – 0.4444*x4*lambda^3 + (2.0*x1 + 2.0*x2)*lambda, lambda*(x1*(exp(- a*kx*0.25i – a*ky*0.433i) + exp(a*kx*0.25i + a*ky*0.433i)) + x2*(exp(- a*kx*0.75i + a*ky*0.433i) + exp(a*kx*0.75i – a*ky*0.433i)))*(0.1667 + 0.2887i) – x4*lambda^3*(exp(- a*kx*0.25i – a*ky*0.433i) + exp(a*kx*0.25i + a*ky*0.433i))*(0.1111 + 0.1925i), lambda*(x1*(exp(- a*kx*0.25i + a*ky*0.433i) + exp(a*kx*0.25i – a*ky*0.433i)) + x2*(exp(- a*kx*0.75i – a*ky*0.433i) + exp(a*kx*0.75i + a*ky*0.433i)))*(0.1667 – 0.2887i) – x4*lambda^3*(exp(- a*kx*0.25i + a*ky*0.433i) + exp(a*kx*0.25i – a*ky*0.433i))*(0.7778 – 1.347i)
– lambda*(x1*(exp(- a*kx*0.25i + a*ky*0.433i) + exp(a*kx*0.25i – a*ky*0.433i)) + x2*(exp(- a*kx*0.75i – a*ky*0.433i) + exp(a*kx*0.75i + a*ky*0.433i)))*(0.3333 + 0.5774i) – x4*lambda^3*(exp(- a*kx*0.25i + a*ky*0.433i) + exp(a*kx*0.25i – a*ky*0.433i))*(0.6667 – 0.3849i) + x4*lambda^3*(exp(- a*kx*0.25i + a*ky*0.433i)*(0.8889 – 1.11e-16i) + exp(a*kx*0.25i – a*ky*0.433i)*(0.8889 – 1.11e-16i)), lambda*(x1*(exp(- a*kx*0.25i – a*ky*0.433i) + exp(a*kx*0.25i + a*ky*0.433i)) + x2*(exp(- a*kx*0.75i + a*ky*0.433i) + exp(a*kx*0.75i – a*ky*0.433i)))*(0.6667 + 1.027e-33i) + x4*lambda^3*(exp(- a*kx*0.25i – a*ky*0.433i) + exp(a*kx*0.25i + a*ky*0.433i))*1.54i – 1.0*x4*lambda^3*(0.4444*exp(- a*kx*0.25i – a*ky*0.433i) + 0.4444*exp(a*kx*0.25i + a*ky*0.433i)), 0, 0.6667*lambda*(x2*(exp(-a*ky*0.866i) + exp(a*ky*0.866i)) + x1*(exp(-a*kx*0.5i) + exp(a*kx*0.5i))) – x4*lambda^3*(exp(-a*kx*0.5i) + exp(a*kx*0.5i))*1.54i – 1.0*x4*lambda^3*(exp(-a*kx*0.5i)*(0.4444 + 1.388e-17i) + exp(a*kx*0.5i)*(0.4444 + 1.388e-17i)), lambda*(x1*(exp(- a*kx*0.25i + a*ky*0.433i) + exp(a*kx*0.25i – a*ky*0.433i)) + x2*(exp(- a*kx*0.75i – a*ky*0.433i) + exp(a*kx*0.75i + a*ky*0.433i)))*(0.1667 – 0.2887i) – x4*lambda^3*(exp(- a*kx*0.25i + a*ky*0.433i) + exp(a*kx*0.25i – a*ky*0.433i))*(0.7778 – 1.347i), lambda*(x1*(exp(- a*kx*0.25i – a*ky*0.433i) + exp(a*kx*0.25i + a*ky*0.433i)) + x2*(exp(- a*kx*0.75i + a*ky*0.433i) + exp(a*kx*0.75i – a*ky*0.433i)))*(0.1667 – 0.2887i) – x4*lambda^3*(exp(- a*kx*0.25i – a*ky*0.433i) + exp(a*kx*0.25i + a*ky*0.433i))*(0.1111 – 0.1925i), – 0.4444*x4*lambda^3 + (2.0*x1 + 2.0*x2)*lambda, lambda*(x2*(exp(-a*ky*0.866i) + exp(a*ky*0.866i)) + x1*(exp(-a*kx*0.5i) + exp(a*kx*0.5i)))*(0.1667 + 0.2887i) – x4*lambda^3*(exp(-a*kx*0.5i) + exp(a*kx*0.5i))*(0.1111 + 0.1925i)
– lambda*(x1*(exp(- a*kx*0.25i – a*ky*0.433i) + exp(a*kx*0.25i + a*ky*0.433i)) + x2*(exp(- a*kx*0.75i + a*ky*0.433i) + exp(a*kx*0.75i – a*ky*0.433i)))*(0.3333 – 0.5774i) – x4*lambda^3*(exp(- a*kx*0.25i – a*ky*0.433i) + exp(a*kx*0.25i + a*ky*0.433i))*(0.2222 + 1.155i) – 1.0*x4*lambda^3*(exp(- a*kx*0.25i – a*ky*0.433i)*(0.8889 – 5.551e-17i) + exp(a*kx*0.25i + a*ky*0.433i)*(0.8889 – 5.551e-17i)), lambda*(x1*(exp(- a*kx*0.25i + a*ky*0.433i) + exp(a*kx*0.25i – a*ky*0.433i)) + x2*(exp(- a*kx*0.75i – a*ky*0.433i) + exp(a*kx*0.75i + a*ky*0.433i)))*(0.6667 + 1.027e-33i) – 0.8889*x4*lambda^3*(exp(- a*kx*0.25i + a*ky*0.433i) + exp(a*kx*0.25i – a*ky*0.433i)) + x4*lambda^3*(0.4444*exp(- a*kx*0.25i + a*ky*0.433i) + 0.4444*exp(a*kx*0.25i – a*ky*0.433i)), 0.6667*lambda*(x2*(exp(-a*ky*0.866i) + exp(a*ky*0.866i)) + x1*(exp(-a*kx*0.5i) + exp(a*kx*0.5i))) – x4*lambda^3*(exp(-a*kx*0.5i) + exp(a*kx*0.5i))*1.54i – 1.0*x4*lambda^3*(exp(-a*kx*0.5i)*(0.4444 + 1.388e-17i) + exp(a*kx*0.5i)*(0.4444 + 1.388e-17i)), 0, lambda*(x1*(exp(- a*kx*0.25i – a*ky*0.433i) + exp(a*kx*0.25i + a*ky*0.433i)) + x2*(exp(- a*kx*0.75i + a*ky*0.433i) + exp(a*kx*0.75i – a*ky*0.433i)))*(0.1667 + 0.2887i) – x4*lambda^3*(exp(- a*kx*0.25i – a*ky*0.433i) + exp(a*kx*0.25i + a*ky*0.433i))*(0.1111 + 0.1925i), lambda*(x1*(exp(- a*kx*0.25i + a*ky*0.433i) + exp(a*kx*0.25i – a*ky*0.433i)) + x2*(exp(- a*kx*0.75i – a*ky*0.433i) + exp(a*kx*0.75i + a*ky*0.433i)))*(0.1667 + 0.2887i) – x4*lambda^3*(exp(- a*kx*0.25i + a*ky*0.433i) + exp(a*kx*0.25i – a*ky*0.433i))*(0.7778 + 1.347i), lambda*(x2*(exp(-a*ky*0.866i) + exp(a*ky*0.866i)) + x1*(exp(-a*kx*0.5i) + exp(a*kx*0.5i)))*(0.1667 – 0.2887i) – x4*lambda^3*(exp(-a*kx*0.5i) + exp(a*kx*0.5i))*(0.1111 – 0.1925i), – 0.4444*x4*lambda^3 + (2.0*x1 + 2.0*x2)*lambda
];
FF = F; % this gives same "Uk" on both computers
FF = F + eye(8)*(0.0001); % this gives different "Uk" on both computers
K = chol(FF,"upper");
Q = K*sigma3*(K’); % Q is same on both computers
[RV,D,~] = eig(Q);
[D,I] = sort(diag(real(D)),’descend’);
RV = RV(:, I);
Uk = RV;
Lk = diag(D);
% more specifically i get these results with "FF = F + eye(8)*(0.0001)":
Uk_on_laptop = [-0.5581 – 0.2520i -0.4914 – 0.1718i -0.1074 – 0.3017i 0.2309 – 0.2705i 0.0962 – 0.0850i -0.1447 – 0.2249i 0.1924 – 0.0017i -0.0000 + 0.0000i
0.4717 + 0.2130i -0.5796 – 0.1237i 0.1084 – 0.2927i -0.2900 + 0.2300i -0.2030 + 0.1332i -0.0739 – 0.1924i 0.2089 – 0.0371i 0.0000 + 0.0000i
-0.0445 – 0.4460i -0.0758 – 0.2465i -0.4168 + 0.3376i -0.5328 + 0.0341i -0.2467 – 0.0430i -0.1713 + 0.1603i 0.0220 + 0.2039i 0.0000 – 0.0000i
0.3210 – 0.2306i -0.2904 + 0.1641i 0.3409 + 0.4428i 0.0806 – 0.4949i 0.0400 – 0.2456i 0.1073 + 0.1394i 0.2399 – 0.1355i 0.0000 – 0.0000i
0.0000 – 0.0000i 0.1304 – 0.0722i -0.2064 + 0.0175i 0.0769 – 0.0629i -0.2355 + 0.1429i 0.2359 – 0.0767i 0.2660 – 0.3031i -0.3953 + 0.6846i
-0.0000 + 0.0000i 0.2267 – 0.0899i -0.2537 + 0.0585i -0.1151 + 0.1335i 0.1507 – 0.1648i 0.2370 – 0.2627i 0.4938 – 0.4630i 0.2283 – 0.3953i
-0.0000 – 0.0000i -0.0689 – 0.1710i 0.0363 – 0.2315i 0.0199 + 0.2998i 0.0948 – 0.6211i -0.0523 + 0.5134i -0.0370 – 0.2328i 0.1614 + 0.2795i
-0.0000 + 0.0000i 0.2904 + 0.0000i 0.1855 + 0.0000i 0.2623 + 0.0000i -0.5286 + 0.0000i -0.5890 + 0.0000i 0.3516 + 0.0000i 0.2500 + 0.0000i];
Uk_on_work = [-0.5581 – 0.2520i -0.4914 – 0.1718i -0.1074 – 0.3017i 0.2309 – 0.2705i -0.0962 + 0.0850i -0.1447 – 0.2249i -0.1924 + 0.0017i -0.0000 + 0.0000i
0.4717 + 0.2130i -0.5796 – 0.1237i 0.1084 – 0.2927i -0.2900 + 0.2300i 0.2030 – 0.1332i -0.0739 – 0.1924i -0.2089 + 0.0371i 0.0000 + 0.0000i
-0.0445 – 0.4460i -0.0758 – 0.2465i -0.4168 + 0.3376i -0.5328 + 0.0341i 0.2467 + 0.0430i -0.1713 + 0.1603i -0.0220 – 0.2039i 0.0000 – 0.0000i
0.3210 – 0.2306i -0.2904 + 0.1641i 0.3409 + 0.4428i 0.0806 – 0.4949i -0.0400 + 0.2456i 0.1073 + 0.1394i -0.2399 + 0.1355i 0.0000 – 0.0000i
0.0000 – 0.0000i 0.1304 – 0.0722i -0.2064 + 0.0175i 0.0769 – 0.0629i 0.2355 – 0.1429i 0.2359 – 0.0767i -0.2660 + 0.3031i -0.3953 + 0.6846i
-0.0000 + 0.0000i 0.2267 – 0.0899i -0.2537 + 0.0585i -0.1151 + 0.1335i -0.1507 + 0.1648i 0.2370 – 0.2627i -0.4938 + 0.4630i 0.2283 – 0.3953i
-0.0000 – 0.0000i -0.0689 – 0.1710i 0.0363 – 0.2315i 0.0199 + 0.2998i -0.0948 + 0.6211i -0.0523 + 0.5134i 0.0370 + 0.2328i 0.1614 + 0.2795i
-0.0000 + 0.0000i 0.2904 + 0.0000i 0.1855 + 0.0000i 0.2623 + 0.0000i 0.5286 + 0.0000i -0.5890 + 0.0000i -0.3516 + 0.0000i 0.2500 + 0.0000i];In the following code, when I add the term eye(8)*(0.0001) to the matrix F, my laptop and my work computer return different values for the variable Uk. Could anyone please help me understand what is happening?
I am using MATLAB R2024a on both computers.
clear; clc;
% constants
a = 1;
x1 = 1;
x2 = 0;
x4 = 0;
lambda = 1;
sigma3 = diag([1 1 1 1 -1 -1 -1 -1]);
kx = -2.0944;
ky = -3.6276;
F = [ – 0.4444*x4*lambda^3 + (2.0*x1 + 2.0*x2)*lambda, – lambda*(x2*(exp(-a*ky*0.866i) + exp(a*ky*0.866i)) + x1*(exp(-a*kx*0.5i) + exp(a*kx*0.5i)))*(0.3333 – 4.082e-17i) – 1.0*x4*lambda^3*(exp(-a*kx*0.5i)*(0.8889 + 5.551e-17i) + exp(a*kx*0.5i)*(0.8889 + 5.551e-17i)) + x4*lambda^3*(exp(-a*kx*0.5i)*(0.8889 + 1.11e-16i) + exp(a*kx*0.5i)*(0.8889 + 1.11e-16i)) + x4*lambda^3*(exp(-a*kx*0.5i)*(0.2222 – 2.721e-17i) + exp(a*kx*0.5i)*(0.2222 – 2.721e-17i)), lambda*(x1*(exp(- a*kx*0.25i + a*ky*0.433i) + exp(a*kx*0.25i – a*ky*0.433i)) + x2*(exp(- a*kx*0.75i – a*ky*0.433i) + exp(a*kx*0.75i + a*ky*0.433i)))*(0.1667 – 0.2887i) – x4*lambda^3*(exp(- a*kx*0.25i + a*ky*0.433i) + exp(a*kx*0.25i – a*ky*0.433i))*(0.7778 – 1.347i), lambda*(x1*(exp(- a*kx*0.25i – a*ky*0.433i) + exp(a*kx*0.25i + a*ky*0.433i)) + x2*(exp(- a*kx*0.75i + a*ky*0.433i) + exp(a*kx*0.75i – a*ky*0.433i)))*(0.1667 + 0.2887i) – x4*lambda^3*(exp(- a*kx*0.25i – a*ky*0.433i) + exp(a*kx*0.25i + a*ky*0.433i))*(0.1111 + 0.1925i), 0, lambda*(x2*(exp(-a*ky*0.866i) + exp(a*ky*0.866i)) + x1*(exp(-a*kx*0.5i) + exp(a*kx*0.5i)))*(0.6667 – 8.164e-17i) + x4*lambda^3*(exp(-a*kx*0.5i) + exp(a*kx*0.5i))*1.54i – 1.0*x4*lambda^3*(exp(-a*kx*0.5i)*(0.4444 – 5.443e-17i) + exp(a*kx*0.5i)*(0.4444 – 5.443e-17i)), – lambda*(x1*(exp(- a*kx*0.25i + a*ky*0.433i) + exp(a*kx*0.25i – a*ky*0.433i)) + x2*(exp(- a*kx*0.75i – a*ky*0.433i) + exp(a*kx*0.75i + a*ky*0.433i)))*(0.3333 – 0.5774i) – x4*lambda^3*(exp(- a*kx*0.25i + a*ky*0.433i) + exp(a*kx*0.25i – a*ky*0.433i))*(0.6667 + 0.3849i) + x4*lambda^3*(exp(- a*kx*0.25i + a*ky*0.433i)*(0.8889 + 1.11e-16i) + exp(a*kx*0.25i – a*ky*0.433i)*(0.8889 + 1.11e-16i)), – lambda*(x1*(exp(- a*kx*0.25i – a*ky*0.433i) + exp(a*kx*0.25i + a*ky*0.433i)) + x2*(exp(- a*kx*0.75i + a*ky*0.433i) + exp(a*kx*0.75i – a*ky*0.433i)))*(0.3333 + 0.5774i) – x4*lambda^3*(exp(- a*kx*0.25i – a*ky*0.433i) + exp(a*kx*0.25i + a*ky*0.433i))*(0.2222 – 1.155i) – 1.0*x4*lambda^3*(exp(- a*kx*0.25i – a*ky*0.433i)*(0.8889 + 5.551e-17i) + exp(a*kx*0.25i + a*ky*0.433i)*(0.8889 + 5.551e-17i))
– lambda*(x2*(exp(-a*ky*0.866i) + exp(a*ky*0.866i)) + x1*(exp(-a*kx*0.5i) + exp(a*kx*0.5i)))*(0.3333 + 4.082e-17i) – 1.0*x4*lambda^3*(exp(-a*kx*0.5i)*(0.8889 – 5.551e-17i) + exp(a*kx*0.5i)*(0.8889 – 5.551e-17i)) + x4*lambda^3*(exp(-a*kx*0.5i)*(0.8889 – 1.11e-16i) + exp(a*kx*0.5i)*(0.8889 – 1.11e-16i)) + x4*lambda^3*(exp(-a*kx*0.5i)*(0.2222 + 2.721e-17i) + exp(a*kx*0.5i)*(0.2222 + 2.721e-17i)), – 0.4444*x4*lambda^3 + (2.0*x1 + 2.0*x2)*lambda, lambda*(x1*(exp(- a*kx*0.25i – a*ky*0.433i) + exp(a*kx*0.25i + a*ky*0.433i)) + x2*(exp(- a*kx*0.75i + a*ky*0.433i) + exp(a*kx*0.75i – a*ky*0.433i)))*(0.1667 – 0.2887i) – x4*lambda^3*(exp(- a*kx*0.25i – a*ky*0.433i) + exp(a*kx*0.25i + a*ky*0.433i))*(0.1111 – 0.1925i), lambda*(x1*(exp(- a*kx*0.25i + a*ky*0.433i) + exp(a*kx*0.25i – a*ky*0.433i)) + x2*(exp(- a*kx*0.75i – a*ky*0.433i) + exp(a*kx*0.75i + a*ky*0.433i)))*(0.1667 + 0.2887i) – x4*lambda^3*(exp(- a*kx*0.25i + a*ky*0.433i) + exp(a*kx*0.25i – a*ky*0.433i))*(0.7778 + 1.347i), lambda*(x2*(exp(-a*ky*0.866i) + exp(a*ky*0.866i)) + x1*(exp(-a*kx*0.5i) + exp(a*kx*0.5i)))*(0.6667 – 8.164e-17i) + x4*lambda^3*(exp(-a*kx*0.5i) + exp(a*kx*0.5i))*1.54i – 1.0*x4*lambda^3*(exp(-a*kx*0.5i)*(0.4444 – 5.443e-17i) + exp(a*kx*0.5i)*(0.4444 – 5.443e-17i)), 0, lambda*(x1*(exp(- a*kx*0.25i – a*ky*0.433i) + exp(a*kx*0.25i + a*ky*0.433i)) + x2*(exp(- a*kx*0.75i + a*ky*0.433i) + exp(a*kx*0.75i – a*ky*0.433i)))*(0.6667 – 1.027e-33i) – x4*lambda^3*(exp(- a*kx*0.25i – a*ky*0.433i) + exp(a*kx*0.25i + a*ky*0.433i))*1.54i – 1.0*x4*lambda^3*(0.4444*exp(- a*kx*0.25i – a*ky*0.433i) + 0.4444*exp(a*kx*0.25i + a*ky*0.433i)), lambda*(x1*(exp(- a*kx*0.25i + a*ky*0.433i) + exp(a*kx*0.25i – a*ky*0.433i)) + x2*(exp(- a*kx*0.75i – a*ky*0.433i) + exp(a*kx*0.75i + a*ky*0.433i)))*(0.6667 – 1.027e-33i) – 0.8889*x4*lambda^3*(exp(- a*kx*0.25i + a*ky*0.433i) + exp(a*kx*0.25i – a*ky*0.433i)) + x4*lambda^3*(0.4444*exp(- a*kx*0.25i + a*ky*0.433i) + 0.4444*exp(a*kx*0.25i – a*ky*0.433i))
lambda*(x1*(exp(- a*kx*0.25i + a*ky*0.433i) + exp(a*kx*0.25i – a*ky*0.433i)) + x2*(exp(- a*kx*0.75i – a*ky*0.433i) + exp(a*kx*0.75i + a*ky*0.433i)))*(0.1667 + 0.2887i) – x4*lambda^3*(exp(- a*kx*0.25i + a*ky*0.433i) + exp(a*kx*0.25i – a*ky*0.433i))*(0.7778 + 1.347i), lambda*(x1*(exp(- a*kx*0.25i – a*ky*0.433i) + exp(a*kx*0.25i + a*ky*0.433i)) + x2*(exp(- a*kx*0.75i + a*ky*0.433i) + exp(a*kx*0.75i – a*ky*0.433i)))*(0.1667 + 0.2887i) – x4*lambda^3*(exp(- a*kx*0.25i – a*ky*0.433i) + exp(a*kx*0.25i + a*ky*0.433i))*(0.1111 + 0.1925i), – 0.4444*x4*lambda^3 + (2.0*x1 + 2.0*x2)*lambda, lambda*(x2*(exp(-a*ky*0.866i) + exp(a*ky*0.866i)) + x1*(exp(-a*kx*0.5i) + exp(a*kx*0.5i)))*(0.1667 – 0.2887i) – x4*lambda^3*(exp(-a*kx*0.5i) + exp(a*kx*0.5i))*(0.1111 – 0.1925i), – lambda*(x1*(exp(- a*kx*0.25i + a*ky*0.433i) + exp(a*kx*0.25i – a*ky*0.433i)) + x2*(exp(- a*kx*0.75i – a*ky*0.433i) + exp(a*kx*0.75i + a*ky*0.433i)))*(0.3333 – 0.5774i) – x4*lambda^3*(exp(- a*kx*0.25i + a*ky*0.433i) + exp(a*kx*0.25i – a*ky*0.433i))*(0.6667 + 0.3849i) + x4*lambda^3*(exp(- a*kx*0.25i + a*ky*0.433i)*(0.8889 + 1.11e-16i) + exp(a*kx*0.25i – a*ky*0.433i)*(0.8889 + 1.11e-16i)), lambda*(x1*(exp(- a*kx*0.25i – a*ky*0.433i) + exp(a*kx*0.25i + a*ky*0.433i)) + x2*(exp(- a*kx*0.75i + a*ky*0.433i) + exp(a*kx*0.75i – a*ky*0.433i)))*(0.6667 – 1.027e-33i) – x4*lambda^3*(exp(- a*kx*0.25i – a*ky*0.433i) + exp(a*kx*0.25i + a*ky*0.433i))*(1.257 + 1.54i), 0, 0.6667*lambda*(x2*(exp(-a*ky*0.866i) + exp(a*ky*0.866i)) + x1*(exp(-a*kx*0.5i) + exp(a*kx*0.5i))) + x4*lambda^3*(exp(-a*kx*0.5i) + exp(a*kx*0.5i))*1.54i – 1.0*x4*lambda^3*(exp(-a*kx*0.5i)*(0.4444 – 1.388e-17i) + exp(a*kx*0.5i)*(0.4444 – 1.388e-17i))
lambda*(x1*(exp(- a*kx*0.25i – a*ky*0.433i) + exp(a*kx*0.25i + a*ky*0.433i)) + x2*(exp(- a*kx*0.75i + a*ky*0.433i) + exp(a*kx*0.75i – a*ky*0.433i)))*(0.1667 – 0.2887i) – x4*lambda^3*(exp(- a*kx*0.25i – a*ky*0.433i) + exp(a*kx*0.25i + a*ky*0.433i))*(0.1111 – 0.1925i), lambda*(x1*(exp(- a*kx*0.25i + a*ky*0.433i) + exp(a*kx*0.25i – a*ky*0.433i)) + x2*(exp(- a*kx*0.75i – a*ky*0.433i) + exp(a*kx*0.75i + a*ky*0.433i)))*(0.1667 – 0.2887i) – x4*lambda^3*(exp(- a*kx*0.25i + a*ky*0.433i) + exp(a*kx*0.25i – a*ky*0.433i))*(0.7778 – 1.347i), lambda*(x2*(exp(-a*ky*0.866i) + exp(a*ky*0.866i)) + x1*(exp(-a*kx*0.5i) + exp(a*kx*0.5i)))*(0.1667 + 0.2887i) – x4*lambda^3*(exp(-a*kx*0.5i) + exp(a*kx*0.5i))*(0.1111 + 0.1925i), – 0.4444*x4*lambda^3 + (2.0*x1 + 2.0*x2)*lambda, – lambda*(x1*(exp(- a*kx*0.25i – a*ky*0.433i) + exp(a*kx*0.25i + a*ky*0.433i)) + x2*(exp(- a*kx*0.75i + a*ky*0.433i) + exp(a*kx*0.75i – a*ky*0.433i)))*(0.3333 + 0.5774i) – x4*lambda^3*(exp(- a*kx*0.25i – a*ky*0.433i) + exp(a*kx*0.25i + a*ky*0.433i))*(0.2222 – 1.155i) – 1.0*x4*lambda^3*(exp(- a*kx*0.25i – a*ky*0.433i)*(0.8889 + 5.551e-17i) + exp(a*kx*0.25i + a*ky*0.433i)*(0.8889 + 5.551e-17i)), lambda*(x1*(exp(- a*kx*0.25i + a*ky*0.433i) + exp(a*kx*0.25i – a*ky*0.433i)) + x2*(exp(- a*kx*0.75i – a*ky*0.433i) + exp(a*kx*0.75i + a*ky*0.433i)))*(0.6667 – 1.027e-33i) – 0.8889*x4*lambda^3*(exp(- a*kx*0.25i + a*ky*0.433i) + exp(a*kx*0.25i – a*ky*0.433i)) + x4*lambda^3*(0.4444*exp(- a*kx*0.25i + a*ky*0.433i) + 0.4444*exp(a*kx*0.25i – a*ky*0.433i)), 0.6667*lambda*(x2*(exp(-a*ky*0.866i) + exp(a*ky*0.866i)) + x1*(exp(-a*kx*0.5i) + exp(a*kx*0.5i))) + x4*lambda^3*(exp(-a*kx*0.5i) + exp(a*kx*0.5i))*1.54i – 1.0*x4*lambda^3*(exp(-a*kx*0.5i)*(0.4444 – 1.388e-17i) + exp(a*kx*0.5i)*(0.4444 – 1.388e-17i)), 0
0, lambda*(x2*(exp(-a*ky*0.866i) + exp(a*ky*0.866i)) + x1*(exp(-a*kx*0.5i) + exp(a*kx*0.5i)))*(0.6667 + 8.164e-17i) – x4*lambda^3*(exp(-a*kx*0.5i) + exp(a*kx*0.5i))*1.54i – 1.0*x4*lambda^3*(exp(-a*kx*0.5i)*(0.4444 + 5.443e-17i) + exp(a*kx*0.5i)*(0.4444 + 5.443e-17i)), – lambda*(x1*(exp(- a*kx*0.25i + a*ky*0.433i) + exp(a*kx*0.25i – a*ky*0.433i)) + x2*(exp(- a*kx*0.75i – a*ky*0.433i) + exp(a*kx*0.75i + a*ky*0.433i)))*(0.3333 + 0.5774i) – x4*lambda^3*(exp(- a*kx*0.25i + a*ky*0.433i) + exp(a*kx*0.25i – a*ky*0.433i))*(0.6667 – 0.3849i) + x4*lambda^3*(exp(- a*kx*0.25i + a*ky*0.433i)*(0.8889 – 1.11e-16i) + exp(a*kx*0.25i – a*ky*0.433i)*(0.8889 – 1.11e-16i)), – lambda*(x1*(exp(- a*kx*0.25i – a*ky*0.433i) + exp(a*kx*0.25i + a*ky*0.433i)) + x2*(exp(- a*kx*0.75i + a*ky*0.433i) + exp(a*kx*0.75i – a*ky*0.433i)))*(0.3333 – 0.5774i) – x4*lambda^3*(exp(- a*kx*0.25i – a*ky*0.433i) + exp(a*kx*0.25i + a*ky*0.433i))*(0.2222 + 1.155i) – 1.0*x4*lambda^3*(exp(- a*kx*0.25i – a*ky*0.433i)*(0.8889 – 5.551e-17i) + exp(a*kx*0.25i + a*ky*0.433i)*(0.8889 – 5.551e-17i)), – 0.4444*x4*lambda^3 + (2.0*x1 + 2.0*x2)*lambda, – lambda*(x2*(exp(-a*ky*0.866i) + exp(a*ky*0.866i)) + x1*(exp(-a*kx*0.5i) + exp(a*kx*0.5i)))*(0.3333 + 4.082e-17i) – 1.0*x4*lambda^3*(exp(-a*kx*0.5i)*(0.8889 – 5.551e-17i) + exp(a*kx*0.5i)*(0.8889 – 5.551e-17i)) + x4*lambda^3*(exp(-a*kx*0.5i)*(0.8889 – 1.11e-16i) + exp(a*kx*0.5i)*(0.8889 – 1.11e-16i)) + x4*lambda^3*(exp(-a*kx*0.5i)*(0.2222 + 2.721e-17i) + exp(a*kx*0.5i)*(0.2222 + 2.721e-17i)), lambda*(x1*(exp(- a*kx*0.25i + a*ky*0.433i) + exp(a*kx*0.25i – a*ky*0.433i)) + x2*(exp(- a*kx*0.75i – a*ky*0.433i) + exp(a*kx*0.75i + a*ky*0.433i)))*(0.1667 + 0.2887i) – x4*lambda^3*(exp(- a*kx*0.25i + a*ky*0.433i) + exp(a*kx*0.25i – a*ky*0.433i))*(0.7778 + 1.347i), lambda*(x1*(exp(- a*kx*0.25i – a*ky*0.433i) + exp(a*kx*0.25i + a*ky*0.433i)) + x2*(exp(- a*kx*0.75i + a*ky*0.433i) + exp(a*kx*0.75i – a*ky*0.433i)))*(0.1667 – 0.2887i) – x4*lambda^3*(exp(- a*kx*0.25i – a*ky*0.433i) + exp(a*kx*0.25i + a*ky*0.433i))*(0.1111 – 0.1925i)
lambda*(x2*(exp(-a*ky*0.866i) + exp(a*ky*0.866i)) + x1*(exp(-a*kx*0.5i) + exp(a*kx*0.5i)))*(0.6667 + 8.164e-17i) – x4*lambda^3*(exp(-a*kx*0.5i) + exp(a*kx*0.5i))*1.54i – 1.0*x4*lambda^3*(exp(-a*kx*0.5i)*(0.4444 + 5.443e-17i) + exp(a*kx*0.5i)*(0.4444 + 5.443e-17i)), 0, lambda*(x1*(exp(- a*kx*0.25i – a*ky*0.433i) + exp(a*kx*0.25i + a*ky*0.433i)) + x2*(exp(- a*kx*0.75i + a*ky*0.433i) + exp(a*kx*0.75i – a*ky*0.433i)))*(0.6667 + 1.027e-33i) – x4*lambda^3*(exp(- a*kx*0.25i – a*ky*0.433i) + exp(a*kx*0.25i + a*ky*0.433i))*(1.257 – 1.54i), lambda*(x1*(exp(- a*kx*0.25i + a*ky*0.433i) + exp(a*kx*0.25i – a*ky*0.433i)) + x2*(exp(- a*kx*0.75i – a*ky*0.433i) + exp(a*kx*0.75i + a*ky*0.433i)))*(0.6667 + 1.027e-33i) – 0.8889*x4*lambda^3*(exp(- a*kx*0.25i + a*ky*0.433i) + exp(a*kx*0.25i – a*ky*0.433i)) + x4*lambda^3*(0.4444*exp(- a*kx*0.25i + a*ky*0.433i) + 0.4444*exp(a*kx*0.25i – a*ky*0.433i)), – lambda*(x2*(exp(-a*ky*0.866i) + exp(a*ky*0.866i)) + x1*(exp(-a*kx*0.5i) + exp(a*kx*0.5i)))*(0.3333 – 4.082e-17i) – 1.0*x4*lambda^3*(exp(-a*kx*0.5i)*(0.8889 + 5.551e-17i) + exp(a*kx*0.5i)*(0.8889 + 5.551e-17i)) + x4*lambda^3*(exp(-a*kx*0.5i)*(0.8889 + 1.11e-16i) + exp(a*kx*0.5i)*(0.8889 + 1.11e-16i)) + x4*lambda^3*(exp(-a*kx*0.5i)*(0.2222 – 2.721e-17i) + exp(a*kx*0.5i)*(0.2222 – 2.721e-17i)), – 0.4444*x4*lambda^3 + (2.0*x1 + 2.0*x2)*lambda, lambda*(x1*(exp(- a*kx*0.25i – a*ky*0.433i) + exp(a*kx*0.25i + a*ky*0.433i)) + x2*(exp(- a*kx*0.75i + a*ky*0.433i) + exp(a*kx*0.75i – a*ky*0.433i)))*(0.1667 + 0.2887i) – x4*lambda^3*(exp(- a*kx*0.25i – a*ky*0.433i) + exp(a*kx*0.25i + a*ky*0.433i))*(0.1111 + 0.1925i), lambda*(x1*(exp(- a*kx*0.25i + a*ky*0.433i) + exp(a*kx*0.25i – a*ky*0.433i)) + x2*(exp(- a*kx*0.75i – a*ky*0.433i) + exp(a*kx*0.75i + a*ky*0.433i)))*(0.1667 – 0.2887i) – x4*lambda^3*(exp(- a*kx*0.25i + a*ky*0.433i) + exp(a*kx*0.25i – a*ky*0.433i))*(0.7778 – 1.347i)
– lambda*(x1*(exp(- a*kx*0.25i + a*ky*0.433i) + exp(a*kx*0.25i – a*ky*0.433i)) + x2*(exp(- a*kx*0.75i – a*ky*0.433i) + exp(a*kx*0.75i + a*ky*0.433i)))*(0.3333 + 0.5774i) – x4*lambda^3*(exp(- a*kx*0.25i + a*ky*0.433i) + exp(a*kx*0.25i – a*ky*0.433i))*(0.6667 – 0.3849i) + x4*lambda^3*(exp(- a*kx*0.25i + a*ky*0.433i)*(0.8889 – 1.11e-16i) + exp(a*kx*0.25i – a*ky*0.433i)*(0.8889 – 1.11e-16i)), lambda*(x1*(exp(- a*kx*0.25i – a*ky*0.433i) + exp(a*kx*0.25i + a*ky*0.433i)) + x2*(exp(- a*kx*0.75i + a*ky*0.433i) + exp(a*kx*0.75i – a*ky*0.433i)))*(0.6667 + 1.027e-33i) + x4*lambda^3*(exp(- a*kx*0.25i – a*ky*0.433i) + exp(a*kx*0.25i + a*ky*0.433i))*1.54i – 1.0*x4*lambda^3*(0.4444*exp(- a*kx*0.25i – a*ky*0.433i) + 0.4444*exp(a*kx*0.25i + a*ky*0.433i)), 0, 0.6667*lambda*(x2*(exp(-a*ky*0.866i) + exp(a*ky*0.866i)) + x1*(exp(-a*kx*0.5i) + exp(a*kx*0.5i))) – x4*lambda^3*(exp(-a*kx*0.5i) + exp(a*kx*0.5i))*1.54i – 1.0*x4*lambda^3*(exp(-a*kx*0.5i)*(0.4444 + 1.388e-17i) + exp(a*kx*0.5i)*(0.4444 + 1.388e-17i)), lambda*(x1*(exp(- a*kx*0.25i + a*ky*0.433i) + exp(a*kx*0.25i – a*ky*0.433i)) + x2*(exp(- a*kx*0.75i – a*ky*0.433i) + exp(a*kx*0.75i + a*ky*0.433i)))*(0.1667 – 0.2887i) – x4*lambda^3*(exp(- a*kx*0.25i + a*ky*0.433i) + exp(a*kx*0.25i – a*ky*0.433i))*(0.7778 – 1.347i), lambda*(x1*(exp(- a*kx*0.25i – a*ky*0.433i) + exp(a*kx*0.25i + a*ky*0.433i)) + x2*(exp(- a*kx*0.75i + a*ky*0.433i) + exp(a*kx*0.75i – a*ky*0.433i)))*(0.1667 – 0.2887i) – x4*lambda^3*(exp(- a*kx*0.25i – a*ky*0.433i) + exp(a*kx*0.25i + a*ky*0.433i))*(0.1111 – 0.1925i), – 0.4444*x4*lambda^3 + (2.0*x1 + 2.0*x2)*lambda, lambda*(x2*(exp(-a*ky*0.866i) + exp(a*ky*0.866i)) + x1*(exp(-a*kx*0.5i) + exp(a*kx*0.5i)))*(0.1667 + 0.2887i) – x4*lambda^3*(exp(-a*kx*0.5i) + exp(a*kx*0.5i))*(0.1111 + 0.1925i)
– lambda*(x1*(exp(- a*kx*0.25i – a*ky*0.433i) + exp(a*kx*0.25i + a*ky*0.433i)) + x2*(exp(- a*kx*0.75i + a*ky*0.433i) + exp(a*kx*0.75i – a*ky*0.433i)))*(0.3333 – 0.5774i) – x4*lambda^3*(exp(- a*kx*0.25i – a*ky*0.433i) + exp(a*kx*0.25i + a*ky*0.433i))*(0.2222 + 1.155i) – 1.0*x4*lambda^3*(exp(- a*kx*0.25i – a*ky*0.433i)*(0.8889 – 5.551e-17i) + exp(a*kx*0.25i + a*ky*0.433i)*(0.8889 – 5.551e-17i)), lambda*(x1*(exp(- a*kx*0.25i + a*ky*0.433i) + exp(a*kx*0.25i – a*ky*0.433i)) + x2*(exp(- a*kx*0.75i – a*ky*0.433i) + exp(a*kx*0.75i + a*ky*0.433i)))*(0.6667 + 1.027e-33i) – 0.8889*x4*lambda^3*(exp(- a*kx*0.25i + a*ky*0.433i) + exp(a*kx*0.25i – a*ky*0.433i)) + x4*lambda^3*(0.4444*exp(- a*kx*0.25i + a*ky*0.433i) + 0.4444*exp(a*kx*0.25i – a*ky*0.433i)), 0.6667*lambda*(x2*(exp(-a*ky*0.866i) + exp(a*ky*0.866i)) + x1*(exp(-a*kx*0.5i) + exp(a*kx*0.5i))) – x4*lambda^3*(exp(-a*kx*0.5i) + exp(a*kx*0.5i))*1.54i – 1.0*x4*lambda^3*(exp(-a*kx*0.5i)*(0.4444 + 1.388e-17i) + exp(a*kx*0.5i)*(0.4444 + 1.388e-17i)), 0, lambda*(x1*(exp(- a*kx*0.25i – a*ky*0.433i) + exp(a*kx*0.25i + a*ky*0.433i)) + x2*(exp(- a*kx*0.75i + a*ky*0.433i) + exp(a*kx*0.75i – a*ky*0.433i)))*(0.1667 + 0.2887i) – x4*lambda^3*(exp(- a*kx*0.25i – a*ky*0.433i) + exp(a*kx*0.25i + a*ky*0.433i))*(0.1111 + 0.1925i), lambda*(x1*(exp(- a*kx*0.25i + a*ky*0.433i) + exp(a*kx*0.25i – a*ky*0.433i)) + x2*(exp(- a*kx*0.75i – a*ky*0.433i) + exp(a*kx*0.75i + a*ky*0.433i)))*(0.1667 + 0.2887i) – x4*lambda^3*(exp(- a*kx*0.25i + a*ky*0.433i) + exp(a*kx*0.25i – a*ky*0.433i))*(0.7778 + 1.347i), lambda*(x2*(exp(-a*ky*0.866i) + exp(a*ky*0.866i)) + x1*(exp(-a*kx*0.5i) + exp(a*kx*0.5i)))*(0.1667 – 0.2887i) – x4*lambda^3*(exp(-a*kx*0.5i) + exp(a*kx*0.5i))*(0.1111 – 0.1925i), – 0.4444*x4*lambda^3 + (2.0*x1 + 2.0*x2)*lambda
];
FF = F; % this gives same "Uk" on both computers
FF = F + eye(8)*(0.0001); % this gives different "Uk" on both computers
K = chol(FF,"upper");
Q = K*sigma3*(K’); % Q is same on both computers
[RV,D,~] = eig(Q);
[D,I] = sort(diag(real(D)),’descend’);
RV = RV(:, I);
Uk = RV;
Lk = diag(D);
% more specifically i get these results with "FF = F + eye(8)*(0.0001)":
Uk_on_laptop = [-0.5581 – 0.2520i -0.4914 – 0.1718i -0.1074 – 0.3017i 0.2309 – 0.2705i 0.0962 – 0.0850i -0.1447 – 0.2249i 0.1924 – 0.0017i -0.0000 + 0.0000i
0.4717 + 0.2130i -0.5796 – 0.1237i 0.1084 – 0.2927i -0.2900 + 0.2300i -0.2030 + 0.1332i -0.0739 – 0.1924i 0.2089 – 0.0371i 0.0000 + 0.0000i
-0.0445 – 0.4460i -0.0758 – 0.2465i -0.4168 + 0.3376i -0.5328 + 0.0341i -0.2467 – 0.0430i -0.1713 + 0.1603i 0.0220 + 0.2039i 0.0000 – 0.0000i
0.3210 – 0.2306i -0.2904 + 0.1641i 0.3409 + 0.4428i 0.0806 – 0.4949i 0.0400 – 0.2456i 0.1073 + 0.1394i 0.2399 – 0.1355i 0.0000 – 0.0000i
0.0000 – 0.0000i 0.1304 – 0.0722i -0.2064 + 0.0175i 0.0769 – 0.0629i -0.2355 + 0.1429i 0.2359 – 0.0767i 0.2660 – 0.3031i -0.3953 + 0.6846i
-0.0000 + 0.0000i 0.2267 – 0.0899i -0.2537 + 0.0585i -0.1151 + 0.1335i 0.1507 – 0.1648i 0.2370 – 0.2627i 0.4938 – 0.4630i 0.2283 – 0.3953i
-0.0000 – 0.0000i -0.0689 – 0.1710i 0.0363 – 0.2315i 0.0199 + 0.2998i 0.0948 – 0.6211i -0.0523 + 0.5134i -0.0370 – 0.2328i 0.1614 + 0.2795i
-0.0000 + 0.0000i 0.2904 + 0.0000i 0.1855 + 0.0000i 0.2623 + 0.0000i -0.5286 + 0.0000i -0.5890 + 0.0000i 0.3516 + 0.0000i 0.2500 + 0.0000i];
Uk_on_work = [-0.5581 – 0.2520i -0.4914 – 0.1718i -0.1074 – 0.3017i 0.2309 – 0.2705i -0.0962 + 0.0850i -0.1447 – 0.2249i -0.1924 + 0.0017i -0.0000 + 0.0000i
0.4717 + 0.2130i -0.5796 – 0.1237i 0.1084 – 0.2927i -0.2900 + 0.2300i 0.2030 – 0.1332i -0.0739 – 0.1924i -0.2089 + 0.0371i 0.0000 + 0.0000i
-0.0445 – 0.4460i -0.0758 – 0.2465i -0.4168 + 0.3376i -0.5328 + 0.0341i 0.2467 + 0.0430i -0.1713 + 0.1603i -0.0220 – 0.2039i 0.0000 – 0.0000i
0.3210 – 0.2306i -0.2904 + 0.1641i 0.3409 + 0.4428i 0.0806 – 0.4949i -0.0400 + 0.2456i 0.1073 + 0.1394i -0.2399 + 0.1355i 0.0000 – 0.0000i
0.0000 – 0.0000i 0.1304 – 0.0722i -0.2064 + 0.0175i 0.0769 – 0.0629i 0.2355 – 0.1429i 0.2359 – 0.0767i -0.2660 + 0.3031i -0.3953 + 0.6846i
-0.0000 + 0.0000i 0.2267 – 0.0899i -0.2537 + 0.0585i -0.1151 + 0.1335i -0.1507 + 0.1648i 0.2370 – 0.2627i -0.4938 + 0.4630i 0.2283 – 0.3953i
-0.0000 – 0.0000i -0.0689 – 0.1710i 0.0363 – 0.2315i 0.0199 + 0.2998i -0.0948 + 0.6211i -0.0523 + 0.5134i 0.0370 + 0.2328i 0.1614 + 0.2795i
-0.0000 + 0.0000i 0.2904 + 0.0000i 0.1855 + 0.0000i 0.2623 + 0.0000i 0.5286 + 0.0000i -0.5890 + 0.0000i -0.3516 + 0.0000i 0.2500 + 0.0000i]; In the following code, when I add the term eye(8)*(0.0001) to the matrix F, my laptop and my work computer return different values for the variable Uk. Could anyone please help me understand what is happening?
I am using MATLAB R2024a on both computers.
clear; clc;
% constants
a = 1;
x1 = 1;
x2 = 0;
x4 = 0;
lambda = 1;
sigma3 = diag([1 1 1 1 -1 -1 -1 -1]);
kx = -2.0944;
ky = -3.6276;
F = [ – 0.4444*x4*lambda^3 + (2.0*x1 + 2.0*x2)*lambda, – lambda*(x2*(exp(-a*ky*0.866i) + exp(a*ky*0.866i)) + x1*(exp(-a*kx*0.5i) + exp(a*kx*0.5i)))*(0.3333 – 4.082e-17i) – 1.0*x4*lambda^3*(exp(-a*kx*0.5i)*(0.8889 + 5.551e-17i) + exp(a*kx*0.5i)*(0.8889 + 5.551e-17i)) + x4*lambda^3*(exp(-a*kx*0.5i)*(0.8889 + 1.11e-16i) + exp(a*kx*0.5i)*(0.8889 + 1.11e-16i)) + x4*lambda^3*(exp(-a*kx*0.5i)*(0.2222 – 2.721e-17i) + exp(a*kx*0.5i)*(0.2222 – 2.721e-17i)), lambda*(x1*(exp(- a*kx*0.25i + a*ky*0.433i) + exp(a*kx*0.25i – a*ky*0.433i)) + x2*(exp(- a*kx*0.75i – a*ky*0.433i) + exp(a*kx*0.75i + a*ky*0.433i)))*(0.1667 – 0.2887i) – x4*lambda^3*(exp(- a*kx*0.25i + a*ky*0.433i) + exp(a*kx*0.25i – a*ky*0.433i))*(0.7778 – 1.347i), lambda*(x1*(exp(- a*kx*0.25i – a*ky*0.433i) + exp(a*kx*0.25i + a*ky*0.433i)) + x2*(exp(- a*kx*0.75i + a*ky*0.433i) + exp(a*kx*0.75i – a*ky*0.433i)))*(0.1667 + 0.2887i) – x4*lambda^3*(exp(- a*kx*0.25i – a*ky*0.433i) + exp(a*kx*0.25i + a*ky*0.433i))*(0.1111 + 0.1925i), 0, lambda*(x2*(exp(-a*ky*0.866i) + exp(a*ky*0.866i)) + x1*(exp(-a*kx*0.5i) + exp(a*kx*0.5i)))*(0.6667 – 8.164e-17i) + x4*lambda^3*(exp(-a*kx*0.5i) + exp(a*kx*0.5i))*1.54i – 1.0*x4*lambda^3*(exp(-a*kx*0.5i)*(0.4444 – 5.443e-17i) + exp(a*kx*0.5i)*(0.4444 – 5.443e-17i)), – lambda*(x1*(exp(- a*kx*0.25i + a*ky*0.433i) + exp(a*kx*0.25i – a*ky*0.433i)) + x2*(exp(- a*kx*0.75i – a*ky*0.433i) + exp(a*kx*0.75i + a*ky*0.433i)))*(0.3333 – 0.5774i) – x4*lambda^3*(exp(- a*kx*0.25i + a*ky*0.433i) + exp(a*kx*0.25i – a*ky*0.433i))*(0.6667 + 0.3849i) + x4*lambda^3*(exp(- a*kx*0.25i + a*ky*0.433i)*(0.8889 + 1.11e-16i) + exp(a*kx*0.25i – a*ky*0.433i)*(0.8889 + 1.11e-16i)), – lambda*(x1*(exp(- a*kx*0.25i – a*ky*0.433i) + exp(a*kx*0.25i + a*ky*0.433i)) + x2*(exp(- a*kx*0.75i + a*ky*0.433i) + exp(a*kx*0.75i – a*ky*0.433i)))*(0.3333 + 0.5774i) – x4*lambda^3*(exp(- a*kx*0.25i – a*ky*0.433i) + exp(a*kx*0.25i + a*ky*0.433i))*(0.2222 – 1.155i) – 1.0*x4*lambda^3*(exp(- a*kx*0.25i – a*ky*0.433i)*(0.8889 + 5.551e-17i) + exp(a*kx*0.25i + a*ky*0.433i)*(0.8889 + 5.551e-17i))
– lambda*(x2*(exp(-a*ky*0.866i) + exp(a*ky*0.866i)) + x1*(exp(-a*kx*0.5i) + exp(a*kx*0.5i)))*(0.3333 + 4.082e-17i) – 1.0*x4*lambda^3*(exp(-a*kx*0.5i)*(0.8889 – 5.551e-17i) + exp(a*kx*0.5i)*(0.8889 – 5.551e-17i)) + x4*lambda^3*(exp(-a*kx*0.5i)*(0.8889 – 1.11e-16i) + exp(a*kx*0.5i)*(0.8889 – 1.11e-16i)) + x4*lambda^3*(exp(-a*kx*0.5i)*(0.2222 + 2.721e-17i) + exp(a*kx*0.5i)*(0.2222 + 2.721e-17i)), – 0.4444*x4*lambda^3 + (2.0*x1 + 2.0*x2)*lambda, lambda*(x1*(exp(- a*kx*0.25i – a*ky*0.433i) + exp(a*kx*0.25i + a*ky*0.433i)) + x2*(exp(- a*kx*0.75i + a*ky*0.433i) + exp(a*kx*0.75i – a*ky*0.433i)))*(0.1667 – 0.2887i) – x4*lambda^3*(exp(- a*kx*0.25i – a*ky*0.433i) + exp(a*kx*0.25i + a*ky*0.433i))*(0.1111 – 0.1925i), lambda*(x1*(exp(- a*kx*0.25i + a*ky*0.433i) + exp(a*kx*0.25i – a*ky*0.433i)) + x2*(exp(- a*kx*0.75i – a*ky*0.433i) + exp(a*kx*0.75i + a*ky*0.433i)))*(0.1667 + 0.2887i) – x4*lambda^3*(exp(- a*kx*0.25i + a*ky*0.433i) + exp(a*kx*0.25i – a*ky*0.433i))*(0.7778 + 1.347i), lambda*(x2*(exp(-a*ky*0.866i) + exp(a*ky*0.866i)) + x1*(exp(-a*kx*0.5i) + exp(a*kx*0.5i)))*(0.6667 – 8.164e-17i) + x4*lambda^3*(exp(-a*kx*0.5i) + exp(a*kx*0.5i))*1.54i – 1.0*x4*lambda^3*(exp(-a*kx*0.5i)*(0.4444 – 5.443e-17i) + exp(a*kx*0.5i)*(0.4444 – 5.443e-17i)), 0, lambda*(x1*(exp(- a*kx*0.25i – a*ky*0.433i) + exp(a*kx*0.25i + a*ky*0.433i)) + x2*(exp(- a*kx*0.75i + a*ky*0.433i) + exp(a*kx*0.75i – a*ky*0.433i)))*(0.6667 – 1.027e-33i) – x4*lambda^3*(exp(- a*kx*0.25i – a*ky*0.433i) + exp(a*kx*0.25i + a*ky*0.433i))*1.54i – 1.0*x4*lambda^3*(0.4444*exp(- a*kx*0.25i – a*ky*0.433i) + 0.4444*exp(a*kx*0.25i + a*ky*0.433i)), lambda*(x1*(exp(- a*kx*0.25i + a*ky*0.433i) + exp(a*kx*0.25i – a*ky*0.433i)) + x2*(exp(- a*kx*0.75i – a*ky*0.433i) + exp(a*kx*0.75i + a*ky*0.433i)))*(0.6667 – 1.027e-33i) – 0.8889*x4*lambda^3*(exp(- a*kx*0.25i + a*ky*0.433i) + exp(a*kx*0.25i – a*ky*0.433i)) + x4*lambda^3*(0.4444*exp(- a*kx*0.25i + a*ky*0.433i) + 0.4444*exp(a*kx*0.25i – a*ky*0.433i))
lambda*(x1*(exp(- a*kx*0.25i + a*ky*0.433i) + exp(a*kx*0.25i – a*ky*0.433i)) + x2*(exp(- a*kx*0.75i – a*ky*0.433i) + exp(a*kx*0.75i + a*ky*0.433i)))*(0.1667 + 0.2887i) – x4*lambda^3*(exp(- a*kx*0.25i + a*ky*0.433i) + exp(a*kx*0.25i – a*ky*0.433i))*(0.7778 + 1.347i), lambda*(x1*(exp(- a*kx*0.25i – a*ky*0.433i) + exp(a*kx*0.25i + a*ky*0.433i)) + x2*(exp(- a*kx*0.75i + a*ky*0.433i) + exp(a*kx*0.75i – a*ky*0.433i)))*(0.1667 + 0.2887i) – x4*lambda^3*(exp(- a*kx*0.25i – a*ky*0.433i) + exp(a*kx*0.25i + a*ky*0.433i))*(0.1111 + 0.1925i), – 0.4444*x4*lambda^3 + (2.0*x1 + 2.0*x2)*lambda, lambda*(x2*(exp(-a*ky*0.866i) + exp(a*ky*0.866i)) + x1*(exp(-a*kx*0.5i) + exp(a*kx*0.5i)))*(0.1667 – 0.2887i) – x4*lambda^3*(exp(-a*kx*0.5i) + exp(a*kx*0.5i))*(0.1111 – 0.1925i), – lambda*(x1*(exp(- a*kx*0.25i + a*ky*0.433i) + exp(a*kx*0.25i – a*ky*0.433i)) + x2*(exp(- a*kx*0.75i – a*ky*0.433i) + exp(a*kx*0.75i + a*ky*0.433i)))*(0.3333 – 0.5774i) – x4*lambda^3*(exp(- a*kx*0.25i + a*ky*0.433i) + exp(a*kx*0.25i – a*ky*0.433i))*(0.6667 + 0.3849i) + x4*lambda^3*(exp(- a*kx*0.25i + a*ky*0.433i)*(0.8889 + 1.11e-16i) + exp(a*kx*0.25i – a*ky*0.433i)*(0.8889 + 1.11e-16i)), lambda*(x1*(exp(- a*kx*0.25i – a*ky*0.433i) + exp(a*kx*0.25i + a*ky*0.433i)) + x2*(exp(- a*kx*0.75i + a*ky*0.433i) + exp(a*kx*0.75i – a*ky*0.433i)))*(0.6667 – 1.027e-33i) – x4*lambda^3*(exp(- a*kx*0.25i – a*ky*0.433i) + exp(a*kx*0.25i + a*ky*0.433i))*(1.257 + 1.54i), 0, 0.6667*lambda*(x2*(exp(-a*ky*0.866i) + exp(a*ky*0.866i)) + x1*(exp(-a*kx*0.5i) + exp(a*kx*0.5i))) + x4*lambda^3*(exp(-a*kx*0.5i) + exp(a*kx*0.5i))*1.54i – 1.0*x4*lambda^3*(exp(-a*kx*0.5i)*(0.4444 – 1.388e-17i) + exp(a*kx*0.5i)*(0.4444 – 1.388e-17i))
lambda*(x1*(exp(- a*kx*0.25i – a*ky*0.433i) + exp(a*kx*0.25i + a*ky*0.433i)) + x2*(exp(- a*kx*0.75i + a*ky*0.433i) + exp(a*kx*0.75i – a*ky*0.433i)))*(0.1667 – 0.2887i) – x4*lambda^3*(exp(- a*kx*0.25i – a*ky*0.433i) + exp(a*kx*0.25i + a*ky*0.433i))*(0.1111 – 0.1925i), lambda*(x1*(exp(- a*kx*0.25i + a*ky*0.433i) + exp(a*kx*0.25i – a*ky*0.433i)) + x2*(exp(- a*kx*0.75i – a*ky*0.433i) + exp(a*kx*0.75i + a*ky*0.433i)))*(0.1667 – 0.2887i) – x4*lambda^3*(exp(- a*kx*0.25i + a*ky*0.433i) + exp(a*kx*0.25i – a*ky*0.433i))*(0.7778 – 1.347i), lambda*(x2*(exp(-a*ky*0.866i) + exp(a*ky*0.866i)) + x1*(exp(-a*kx*0.5i) + exp(a*kx*0.5i)))*(0.1667 + 0.2887i) – x4*lambda^3*(exp(-a*kx*0.5i) + exp(a*kx*0.5i))*(0.1111 + 0.1925i), – 0.4444*x4*lambda^3 + (2.0*x1 + 2.0*x2)*lambda, – lambda*(x1*(exp(- a*kx*0.25i – a*ky*0.433i) + exp(a*kx*0.25i + a*ky*0.433i)) + x2*(exp(- a*kx*0.75i + a*ky*0.433i) + exp(a*kx*0.75i – a*ky*0.433i)))*(0.3333 + 0.5774i) – x4*lambda^3*(exp(- a*kx*0.25i – a*ky*0.433i) + exp(a*kx*0.25i + a*ky*0.433i))*(0.2222 – 1.155i) – 1.0*x4*lambda^3*(exp(- a*kx*0.25i – a*ky*0.433i)*(0.8889 + 5.551e-17i) + exp(a*kx*0.25i + a*ky*0.433i)*(0.8889 + 5.551e-17i)), lambda*(x1*(exp(- a*kx*0.25i + a*ky*0.433i) + exp(a*kx*0.25i – a*ky*0.433i)) + x2*(exp(- a*kx*0.75i – a*ky*0.433i) + exp(a*kx*0.75i + a*ky*0.433i)))*(0.6667 – 1.027e-33i) – 0.8889*x4*lambda^3*(exp(- a*kx*0.25i + a*ky*0.433i) + exp(a*kx*0.25i – a*ky*0.433i)) + x4*lambda^3*(0.4444*exp(- a*kx*0.25i + a*ky*0.433i) + 0.4444*exp(a*kx*0.25i – a*ky*0.433i)), 0.6667*lambda*(x2*(exp(-a*ky*0.866i) + exp(a*ky*0.866i)) + x1*(exp(-a*kx*0.5i) + exp(a*kx*0.5i))) + x4*lambda^3*(exp(-a*kx*0.5i) + exp(a*kx*0.5i))*1.54i – 1.0*x4*lambda^3*(exp(-a*kx*0.5i)*(0.4444 – 1.388e-17i) + exp(a*kx*0.5i)*(0.4444 – 1.388e-17i)), 0
0, lambda*(x2*(exp(-a*ky*0.866i) + exp(a*ky*0.866i)) + x1*(exp(-a*kx*0.5i) + exp(a*kx*0.5i)))*(0.6667 + 8.164e-17i) – x4*lambda^3*(exp(-a*kx*0.5i) + exp(a*kx*0.5i))*1.54i – 1.0*x4*lambda^3*(exp(-a*kx*0.5i)*(0.4444 + 5.443e-17i) + exp(a*kx*0.5i)*(0.4444 + 5.443e-17i)), – lambda*(x1*(exp(- a*kx*0.25i + a*ky*0.433i) + exp(a*kx*0.25i – a*ky*0.433i)) + x2*(exp(- a*kx*0.75i – a*ky*0.433i) + exp(a*kx*0.75i + a*ky*0.433i)))*(0.3333 + 0.5774i) – x4*lambda^3*(exp(- a*kx*0.25i + a*ky*0.433i) + exp(a*kx*0.25i – a*ky*0.433i))*(0.6667 – 0.3849i) + x4*lambda^3*(exp(- a*kx*0.25i + a*ky*0.433i)*(0.8889 – 1.11e-16i) + exp(a*kx*0.25i – a*ky*0.433i)*(0.8889 – 1.11e-16i)), – lambda*(x1*(exp(- a*kx*0.25i – a*ky*0.433i) + exp(a*kx*0.25i + a*ky*0.433i)) + x2*(exp(- a*kx*0.75i + a*ky*0.433i) + exp(a*kx*0.75i – a*ky*0.433i)))*(0.3333 – 0.5774i) – x4*lambda^3*(exp(- a*kx*0.25i – a*ky*0.433i) + exp(a*kx*0.25i + a*ky*0.433i))*(0.2222 + 1.155i) – 1.0*x4*lambda^3*(exp(- a*kx*0.25i – a*ky*0.433i)*(0.8889 – 5.551e-17i) + exp(a*kx*0.25i + a*ky*0.433i)*(0.8889 – 5.551e-17i)), – 0.4444*x4*lambda^3 + (2.0*x1 + 2.0*x2)*lambda, – lambda*(x2*(exp(-a*ky*0.866i) + exp(a*ky*0.866i)) + x1*(exp(-a*kx*0.5i) + exp(a*kx*0.5i)))*(0.3333 + 4.082e-17i) – 1.0*x4*lambda^3*(exp(-a*kx*0.5i)*(0.8889 – 5.551e-17i) + exp(a*kx*0.5i)*(0.8889 – 5.551e-17i)) + x4*lambda^3*(exp(-a*kx*0.5i)*(0.8889 – 1.11e-16i) + exp(a*kx*0.5i)*(0.8889 – 1.11e-16i)) + x4*lambda^3*(exp(-a*kx*0.5i)*(0.2222 + 2.721e-17i) + exp(a*kx*0.5i)*(0.2222 + 2.721e-17i)), lambda*(x1*(exp(- a*kx*0.25i + a*ky*0.433i) + exp(a*kx*0.25i – a*ky*0.433i)) + x2*(exp(- a*kx*0.75i – a*ky*0.433i) + exp(a*kx*0.75i + a*ky*0.433i)))*(0.1667 + 0.2887i) – x4*lambda^3*(exp(- a*kx*0.25i + a*ky*0.433i) + exp(a*kx*0.25i – a*ky*0.433i))*(0.7778 + 1.347i), lambda*(x1*(exp(- a*kx*0.25i – a*ky*0.433i) + exp(a*kx*0.25i + a*ky*0.433i)) + x2*(exp(- a*kx*0.75i + a*ky*0.433i) + exp(a*kx*0.75i – a*ky*0.433i)))*(0.1667 – 0.2887i) – x4*lambda^3*(exp(- a*kx*0.25i – a*ky*0.433i) + exp(a*kx*0.25i + a*ky*0.433i))*(0.1111 – 0.1925i)
lambda*(x2*(exp(-a*ky*0.866i) + exp(a*ky*0.866i)) + x1*(exp(-a*kx*0.5i) + exp(a*kx*0.5i)))*(0.6667 + 8.164e-17i) – x4*lambda^3*(exp(-a*kx*0.5i) + exp(a*kx*0.5i))*1.54i – 1.0*x4*lambda^3*(exp(-a*kx*0.5i)*(0.4444 + 5.443e-17i) + exp(a*kx*0.5i)*(0.4444 + 5.443e-17i)), 0, lambda*(x1*(exp(- a*kx*0.25i – a*ky*0.433i) + exp(a*kx*0.25i + a*ky*0.433i)) + x2*(exp(- a*kx*0.75i + a*ky*0.433i) + exp(a*kx*0.75i – a*ky*0.433i)))*(0.6667 + 1.027e-33i) – x4*lambda^3*(exp(- a*kx*0.25i – a*ky*0.433i) + exp(a*kx*0.25i + a*ky*0.433i))*(1.257 – 1.54i), lambda*(x1*(exp(- a*kx*0.25i + a*ky*0.433i) + exp(a*kx*0.25i – a*ky*0.433i)) + x2*(exp(- a*kx*0.75i – a*ky*0.433i) + exp(a*kx*0.75i + a*ky*0.433i)))*(0.6667 + 1.027e-33i) – 0.8889*x4*lambda^3*(exp(- a*kx*0.25i + a*ky*0.433i) + exp(a*kx*0.25i – a*ky*0.433i)) + x4*lambda^3*(0.4444*exp(- a*kx*0.25i + a*ky*0.433i) + 0.4444*exp(a*kx*0.25i – a*ky*0.433i)), – lambda*(x2*(exp(-a*ky*0.866i) + exp(a*ky*0.866i)) + x1*(exp(-a*kx*0.5i) + exp(a*kx*0.5i)))*(0.3333 – 4.082e-17i) – 1.0*x4*lambda^3*(exp(-a*kx*0.5i)*(0.8889 + 5.551e-17i) + exp(a*kx*0.5i)*(0.8889 + 5.551e-17i)) + x4*lambda^3*(exp(-a*kx*0.5i)*(0.8889 + 1.11e-16i) + exp(a*kx*0.5i)*(0.8889 + 1.11e-16i)) + x4*lambda^3*(exp(-a*kx*0.5i)*(0.2222 – 2.721e-17i) + exp(a*kx*0.5i)*(0.2222 – 2.721e-17i)), – 0.4444*x4*lambda^3 + (2.0*x1 + 2.0*x2)*lambda, lambda*(x1*(exp(- a*kx*0.25i – a*ky*0.433i) + exp(a*kx*0.25i + a*ky*0.433i)) + x2*(exp(- a*kx*0.75i + a*ky*0.433i) + exp(a*kx*0.75i – a*ky*0.433i)))*(0.1667 + 0.2887i) – x4*lambda^3*(exp(- a*kx*0.25i – a*ky*0.433i) + exp(a*kx*0.25i + a*ky*0.433i))*(0.1111 + 0.1925i), lambda*(x1*(exp(- a*kx*0.25i + a*ky*0.433i) + exp(a*kx*0.25i – a*ky*0.433i)) + x2*(exp(- a*kx*0.75i – a*ky*0.433i) + exp(a*kx*0.75i + a*ky*0.433i)))*(0.1667 – 0.2887i) – x4*lambda^3*(exp(- a*kx*0.25i + a*ky*0.433i) + exp(a*kx*0.25i – a*ky*0.433i))*(0.7778 – 1.347i)
– lambda*(x1*(exp(- a*kx*0.25i + a*ky*0.433i) + exp(a*kx*0.25i – a*ky*0.433i)) + x2*(exp(- a*kx*0.75i – a*ky*0.433i) + exp(a*kx*0.75i + a*ky*0.433i)))*(0.3333 + 0.5774i) – x4*lambda^3*(exp(- a*kx*0.25i + a*ky*0.433i) + exp(a*kx*0.25i – a*ky*0.433i))*(0.6667 – 0.3849i) + x4*lambda^3*(exp(- a*kx*0.25i + a*ky*0.433i)*(0.8889 – 1.11e-16i) + exp(a*kx*0.25i – a*ky*0.433i)*(0.8889 – 1.11e-16i)), lambda*(x1*(exp(- a*kx*0.25i – a*ky*0.433i) + exp(a*kx*0.25i + a*ky*0.433i)) + x2*(exp(- a*kx*0.75i + a*ky*0.433i) + exp(a*kx*0.75i – a*ky*0.433i)))*(0.6667 + 1.027e-33i) + x4*lambda^3*(exp(- a*kx*0.25i – a*ky*0.433i) + exp(a*kx*0.25i + a*ky*0.433i))*1.54i – 1.0*x4*lambda^3*(0.4444*exp(- a*kx*0.25i – a*ky*0.433i) + 0.4444*exp(a*kx*0.25i + a*ky*0.433i)), 0, 0.6667*lambda*(x2*(exp(-a*ky*0.866i) + exp(a*ky*0.866i)) + x1*(exp(-a*kx*0.5i) + exp(a*kx*0.5i))) – x4*lambda^3*(exp(-a*kx*0.5i) + exp(a*kx*0.5i))*1.54i – 1.0*x4*lambda^3*(exp(-a*kx*0.5i)*(0.4444 + 1.388e-17i) + exp(a*kx*0.5i)*(0.4444 + 1.388e-17i)), lambda*(x1*(exp(- a*kx*0.25i + a*ky*0.433i) + exp(a*kx*0.25i – a*ky*0.433i)) + x2*(exp(- a*kx*0.75i – a*ky*0.433i) + exp(a*kx*0.75i + a*ky*0.433i)))*(0.1667 – 0.2887i) – x4*lambda^3*(exp(- a*kx*0.25i + a*ky*0.433i) + exp(a*kx*0.25i – a*ky*0.433i))*(0.7778 – 1.347i), lambda*(x1*(exp(- a*kx*0.25i – a*ky*0.433i) + exp(a*kx*0.25i + a*ky*0.433i)) + x2*(exp(- a*kx*0.75i + a*ky*0.433i) + exp(a*kx*0.75i – a*ky*0.433i)))*(0.1667 – 0.2887i) – x4*lambda^3*(exp(- a*kx*0.25i – a*ky*0.433i) + exp(a*kx*0.25i + a*ky*0.433i))*(0.1111 – 0.1925i), – 0.4444*x4*lambda^3 + (2.0*x1 + 2.0*x2)*lambda, lambda*(x2*(exp(-a*ky*0.866i) + exp(a*ky*0.866i)) + x1*(exp(-a*kx*0.5i) + exp(a*kx*0.5i)))*(0.1667 + 0.2887i) – x4*lambda^3*(exp(-a*kx*0.5i) + exp(a*kx*0.5i))*(0.1111 + 0.1925i)
– lambda*(x1*(exp(- a*kx*0.25i – a*ky*0.433i) + exp(a*kx*0.25i + a*ky*0.433i)) + x2*(exp(- a*kx*0.75i + a*ky*0.433i) + exp(a*kx*0.75i – a*ky*0.433i)))*(0.3333 – 0.5774i) – x4*lambda^3*(exp(- a*kx*0.25i – a*ky*0.433i) + exp(a*kx*0.25i + a*ky*0.433i))*(0.2222 + 1.155i) – 1.0*x4*lambda^3*(exp(- a*kx*0.25i – a*ky*0.433i)*(0.8889 – 5.551e-17i) + exp(a*kx*0.25i + a*ky*0.433i)*(0.8889 – 5.551e-17i)), lambda*(x1*(exp(- a*kx*0.25i + a*ky*0.433i) + exp(a*kx*0.25i – a*ky*0.433i)) + x2*(exp(- a*kx*0.75i – a*ky*0.433i) + exp(a*kx*0.75i + a*ky*0.433i)))*(0.6667 + 1.027e-33i) – 0.8889*x4*lambda^3*(exp(- a*kx*0.25i + a*ky*0.433i) + exp(a*kx*0.25i – a*ky*0.433i)) + x4*lambda^3*(0.4444*exp(- a*kx*0.25i + a*ky*0.433i) + 0.4444*exp(a*kx*0.25i – a*ky*0.433i)), 0.6667*lambda*(x2*(exp(-a*ky*0.866i) + exp(a*ky*0.866i)) + x1*(exp(-a*kx*0.5i) + exp(a*kx*0.5i))) – x4*lambda^3*(exp(-a*kx*0.5i) + exp(a*kx*0.5i))*1.54i – 1.0*x4*lambda^3*(exp(-a*kx*0.5i)*(0.4444 + 1.388e-17i) + exp(a*kx*0.5i)*(0.4444 + 1.388e-17i)), 0, lambda*(x1*(exp(- a*kx*0.25i – a*ky*0.433i) + exp(a*kx*0.25i + a*ky*0.433i)) + x2*(exp(- a*kx*0.75i + a*ky*0.433i) + exp(a*kx*0.75i – a*ky*0.433i)))*(0.1667 + 0.2887i) – x4*lambda^3*(exp(- a*kx*0.25i – a*ky*0.433i) + exp(a*kx*0.25i + a*ky*0.433i))*(0.1111 + 0.1925i), lambda*(x1*(exp(- a*kx*0.25i + a*ky*0.433i) + exp(a*kx*0.25i – a*ky*0.433i)) + x2*(exp(- a*kx*0.75i – a*ky*0.433i) + exp(a*kx*0.75i + a*ky*0.433i)))*(0.1667 + 0.2887i) – x4*lambda^3*(exp(- a*kx*0.25i + a*ky*0.433i) + exp(a*kx*0.25i – a*ky*0.433i))*(0.7778 + 1.347i), lambda*(x2*(exp(-a*ky*0.866i) + exp(a*ky*0.866i)) + x1*(exp(-a*kx*0.5i) + exp(a*kx*0.5i)))*(0.1667 – 0.2887i) – x4*lambda^3*(exp(-a*kx*0.5i) + exp(a*kx*0.5i))*(0.1111 – 0.1925i), – 0.4444*x4*lambda^3 + (2.0*x1 + 2.0*x2)*lambda
];
FF = F; % this gives same "Uk" on both computers
FF = F + eye(8)*(0.0001); % this gives different "Uk" on both computers
K = chol(FF,"upper");
Q = K*sigma3*(K’); % Q is same on both computers
[RV,D,~] = eig(Q);
[D,I] = sort(diag(real(D)),’descend’);
RV = RV(:, I);
Uk = RV;
Lk = diag(D);
% more specifically i get these results with "FF = F + eye(8)*(0.0001)":
Uk_on_laptop = [-0.5581 – 0.2520i -0.4914 – 0.1718i -0.1074 – 0.3017i 0.2309 – 0.2705i 0.0962 – 0.0850i -0.1447 – 0.2249i 0.1924 – 0.0017i -0.0000 + 0.0000i
0.4717 + 0.2130i -0.5796 – 0.1237i 0.1084 – 0.2927i -0.2900 + 0.2300i -0.2030 + 0.1332i -0.0739 – 0.1924i 0.2089 – 0.0371i 0.0000 + 0.0000i
-0.0445 – 0.4460i -0.0758 – 0.2465i -0.4168 + 0.3376i -0.5328 + 0.0341i -0.2467 – 0.0430i -0.1713 + 0.1603i 0.0220 + 0.2039i 0.0000 – 0.0000i
0.3210 – 0.2306i -0.2904 + 0.1641i 0.3409 + 0.4428i 0.0806 – 0.4949i 0.0400 – 0.2456i 0.1073 + 0.1394i 0.2399 – 0.1355i 0.0000 – 0.0000i
0.0000 – 0.0000i 0.1304 – 0.0722i -0.2064 + 0.0175i 0.0769 – 0.0629i -0.2355 + 0.1429i 0.2359 – 0.0767i 0.2660 – 0.3031i -0.3953 + 0.6846i
-0.0000 + 0.0000i 0.2267 – 0.0899i -0.2537 + 0.0585i -0.1151 + 0.1335i 0.1507 – 0.1648i 0.2370 – 0.2627i 0.4938 – 0.4630i 0.2283 – 0.3953i
-0.0000 – 0.0000i -0.0689 – 0.1710i 0.0363 – 0.2315i 0.0199 + 0.2998i 0.0948 – 0.6211i -0.0523 + 0.5134i -0.0370 – 0.2328i 0.1614 + 0.2795i
-0.0000 + 0.0000i 0.2904 + 0.0000i 0.1855 + 0.0000i 0.2623 + 0.0000i -0.5286 + 0.0000i -0.5890 + 0.0000i 0.3516 + 0.0000i 0.2500 + 0.0000i];
Uk_on_work = [-0.5581 – 0.2520i -0.4914 – 0.1718i -0.1074 – 0.3017i 0.2309 – 0.2705i -0.0962 + 0.0850i -0.1447 – 0.2249i -0.1924 + 0.0017i -0.0000 + 0.0000i
0.4717 + 0.2130i -0.5796 – 0.1237i 0.1084 – 0.2927i -0.2900 + 0.2300i 0.2030 – 0.1332i -0.0739 – 0.1924i -0.2089 + 0.0371i 0.0000 + 0.0000i
-0.0445 – 0.4460i -0.0758 – 0.2465i -0.4168 + 0.3376i -0.5328 + 0.0341i 0.2467 + 0.0430i -0.1713 + 0.1603i -0.0220 – 0.2039i 0.0000 – 0.0000i
0.3210 – 0.2306i -0.2904 + 0.1641i 0.3409 + 0.4428i 0.0806 – 0.4949i -0.0400 + 0.2456i 0.1073 + 0.1394i -0.2399 + 0.1355i 0.0000 – 0.0000i
0.0000 – 0.0000i 0.1304 – 0.0722i -0.2064 + 0.0175i 0.0769 – 0.0629i 0.2355 – 0.1429i 0.2359 – 0.0767i -0.2660 + 0.3031i -0.3953 + 0.6846i
-0.0000 + 0.0000i 0.2267 – 0.0899i -0.2537 + 0.0585i -0.1151 + 0.1335i -0.1507 + 0.1648i 0.2370 – 0.2627i -0.4938 + 0.4630i 0.2283 – 0.3953i
-0.0000 – 0.0000i -0.0689 – 0.1710i 0.0363 – 0.2315i 0.0199 + 0.2998i -0.0948 + 0.6211i -0.0523 + 0.5134i 0.0370 + 0.2328i 0.1614 + 0.2795i
-0.0000 + 0.0000i 0.2904 + 0.0000i 0.1855 + 0.0000i 0.2623 + 0.0000i 0.5286 + 0.0000i -0.5890 + 0.0000i -0.3516 + 0.0000i 0.2500 + 0.0000i]; system, eigenvalue, eig MATLAB Answers — New Questions
Answer to Moving Windows section of the “Calculations with Vectors and Matrices” online course is identical to solution but marked wrong. How to get credit?
I’m taking the "Calculations with Vectors and Matrices" self-paced online course, but I’m not getting due credit for the section on Moving WIndow Calculations. I have screenshots to prove that even an answer that is identical to the one under "See solution" (which I reached indenpendently) is marked as wrong, saying I haven’t added minUsage and maxUsage to the plot. I’ve tried many different ways of recalculating, reformatting, and resubmitting Task 4, including eliminating the section break between the problem setup and Task 1 because I suspected that the duplication of the original plot from the setup to Task 1 might be part of the issue, but even that didn’t fix it. At this point, the only way I can get full credit for the course is if someone on the admin side can override the spurious error.I’m taking the "Calculations with Vectors and Matrices" self-paced online course, but I’m not getting due credit for the section on Moving WIndow Calculations. I have screenshots to prove that even an answer that is identical to the one under "See solution" (which I reached indenpendently) is marked as wrong, saying I haven’t added minUsage and maxUsage to the plot. I’ve tried many different ways of recalculating, reformatting, and resubmitting Task 4, including eliminating the section break between the problem setup and Task 1 because I suspected that the duplication of the original plot from the setup to Task 1 might be part of the issue, but even that didn’t fix it. At this point, the only way I can get full credit for the course is if someone on the admin side can override the spurious error. I’m taking the "Calculations with Vectors and Matrices" self-paced online course, but I’m not getting due credit for the section on Moving WIndow Calculations. I have screenshots to prove that even an answer that is identical to the one under "See solution" (which I reached indenpendently) is marked as wrong, saying I haven’t added minUsage and maxUsage to the plot. I’ve tried many different ways of recalculating, reformatting, and resubmitting Task 4, including eliminating the section break between the problem setup and Task 1 because I suspected that the duplication of the original plot from the setup to Task 1 might be part of the issue, but even that didn’t fix it. At this point, the only way I can get full credit for the course is if someone on the admin side can override the spurious error. online course, error, self-paced, moving window MATLAB Answers — New Questions
Data in msec is not displayed
Hello,
I am recording data each 100msec and send it in a bulk data JSON each 30 sec. I only receave data in thingspeak with a time stamp of 1 second, and not those in between each second (I mis 9 measurements each second)
part of the JSON string:
[{ "created_at":"2024-08-12 17:10:55.797Z","field1":817},{ "created_at":"2024-08-12 17:10:55.920Z","field1":864},{ "created_at":"2024-08-12 17:10:56.043Z","field1":856}…
is there a way to visualise all the data?Hello,
I am recording data each 100msec and send it in a bulk data JSON each 30 sec. I only receave data in thingspeak with a time stamp of 1 second, and not those in between each second (I mis 9 measurements each second)
part of the JSON string:
[{ "created_at":"2024-08-12 17:10:55.797Z","field1":817},{ "created_at":"2024-08-12 17:10:55.920Z","field1":864},{ "created_at":"2024-08-12 17:10:56.043Z","field1":856}…
is there a way to visualise all the data? Hello,
I am recording data each 100msec and send it in a bulk data JSON each 30 sec. I only receave data in thingspeak with a time stamp of 1 second, and not those in between each second (I mis 9 measurements each second)
part of the JSON string:
[{ "created_at":"2024-08-12 17:10:55.797Z","field1":817},{ "created_at":"2024-08-12 17:10:55.920Z","field1":864},{ "created_at":"2024-08-12 17:10:56.043Z","field1":856}…
is there a way to visualise all the data? json, msec MATLAB Answers — New Questions
Combinations
Hello. I need some help,
Hello. I need some help, I want to know if there is a way to : If I have the following table, I want to extract all combination with the values….. Greece, USA = 1000Greece, UK=2000, Spain, USA= 400 etc…. USA UkGreece. 1000 2000Spain. 400 5000Italy. 450 800 Read More
