As we all know, Optuna is a well-regarded hyperparameter optimization framework that is independent of any machine learning framework and is very easy to use in Python. However, my cost object-function is in MATLAB, which typically belongs to black-box optimization, and I am unsure how to use the Optuna library.
I also know that the Statistics and Machine Learning Toolbox has techniques like random search, grid search, and Bayesian hyperparameter optimization, but they haven’t performed very well. The Global Optimization Toolbox can use heuristic search algorithms such as GA and PSO, but I am limited by the high computational cost of the cost function. I also tried surrogateopt. Although the iteration speed is relatively fast, it tends to get stuck in local optima, causing subsequent iterations to nearly stop. Overall, it doesn’t perform as well as the PSO algorithm! Therefore, I would like to explore the performance of the Optuna library.As we all know, Optuna is a well-regarded hyperparameter optimization framework that is independent of any machine learning framework and is very easy to use in Python. However, my cost object-function is in MATLAB, which typically belongs to black-box optimization, and I am unsure how to use the Optuna library.
I also know that the Statistics and Machine Learning Toolbox has techniques like random search, grid search, and Bayesian hyperparameter optimization, but they haven’t performed very well. The Global Optimization Toolbox can use heuristic search algorithms such as GA and PSO, but I am limited by the high computational cost of the cost function. I also tried surrogateopt. Although the iteration speed is relatively fast, it tends to get stuck in local optima, causing subsequent iterations to nearly stop. Overall, it doesn’t perform as well as the PSO algorithm! Therefore, I would like to explore the performance of the Optuna library. As we all know, Optuna is a well-regarded hyperparameter optimization framework that is independent of any machine learning framework and is very easy to use in Python. However, my cost object-function is in MATLAB, which typically belongs to black-box optimization, and I am unsure how to use the Optuna library.
I also know that the Statistics and Machine Learning Toolbox has techniques like random search, grid search, and Bayesian hyperparameter optimization, but they haven’t performed very well. The Global Optimization Toolbox can use heuristic search algorithms such as GA and PSO, but I am limited by the high computational cost of the cost function. I also tried surrogateopt. Although the iteration speed is relatively fast, it tends to get stuck in local optima, causing subsequent iterations to nearly stop. Overall, it doesn’t perform as well as the PSO algorithm! Therefore, I would like to explore the performance of the Optuna library. hyperparameter optimization, statistics, machine learning MATLAB Answers — New Questions

​