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MohEsmail143 / numerical-optimization-techniques

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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
  • Updated Mar 12, 2024
  • Jupyter Notebook

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