A set of Jupyter notebooks that investigate and compare the performance of several numerical optimization techniques, both unconstrained (univariate search, Powell's method and Gradient Descent (fixed step and optimal step)) and constrained (Exterior Penalty method).
python
optimization
jupyter-notebook
conda
constrained-optimization
matplotlib
gradient-descent
optimization-methods
miniconda
benchmark-functions
unconstrained-optimization
rosenbrock-function
golden-section-search
dejong
easom
rastringin-function
exterior-penalty-function-method
brainin
univariate-search
powell-method
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Updated
Mar 12, 2024 - Jupyter Notebook