Implementation of nonlinear Optimization Algorithms in Python

Modern Fortran Refactoring of L-BFGS-B Nonlinear Optimization Code

This is a c++ implementation of the BFGS algorithm.

Jupyter notebooks, scripts, and results associated with the paper Visualization of Optimization Algorithms by Marco Morais (Morais, 2020).

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

Convex, Nonsmooth, Nonlinear Optimization Solver and Problems

Binary Logistic Regression Analysis using the Broyden-Fletcher-Goldfarb-Shanno Algorithm on the Quasi-Newton Method

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

Logistic regression on COVID-19 data using BFGS algorithm

R code implementing BFGS Quasi-Newton Minimization Method

Pomodoro: Progressive Decomposition Methods with Acceleration

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

Linear-Parabolic-Expotential-Logarithmic