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Implementation of nonlinear Optimization Algorithms in Python
Modern Fortran Refactoring of L-BFGS-B Nonlinear Optimization Code
Implemented optimization algorithms, including Momentum, AdaGrad, RMSProp, and Adam, from scratch using only NumPy in Python. Implemented the Broyden-Fletcher-Goldfarb-Shanno (BFGS) optimizer and conducted a comparative analysis of its results with those obtained using Adam.
Contains a mathematical optimization project implemented in Python
This is an implementation of different optimization algorithms such as: - Gradient Descent (stochastic - mini-batch - batch) - Momentum - NAG - Adagrad - RMS-prop - BFGS - Adam Also, most of them are implemented in vectorized form for multi-variate problems
Binary Logistic Regression Analysis using the Broyden-Fletcher-Goldfarb-Shanno Algorithm on the Quasi-Newton Method
Jupyter notebooks, scripts, and results associated with the paper Visualization of Optimization Algorithms by Marco Morais (Morais, 2020).
Logistic regression on COVID-19 data using BFGS algorithm
Convex, Nonsmooth, Nonlinear Optimization Solver and Problems
R code implementing BFGS Quasi-Newton Minimization Method
Linear-Parabolic-Expotential-Logarithmic
This project summarizes the learning process of optimization methods, attempting to start from the original mathematical formulas and write Python code to understand the principles of the methods.
R function bfgs( ) implementing the BFGS quasi-Newton minimization method