Modern Fortran Refactoring of L-BFGS-B Nonlinear Optimization Code
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Updated
Jan 23, 2024 - Fortran
Modern Fortran Refactoring of L-BFGS-B Nonlinear Optimization Code
Implementation of nonlinear Optimization Algorithms in Python
This is a c++ implementation of the BFGS algorithm.
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
Jupyter notebooks, scripts, and results associated with the paper Visualization of Optimization Algorithms by Marco Morais (Morais, 2020).
Convex, Nonsmooth, Nonlinear Optimization Solver and Problems
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
Binary Logistic Regression Analysis using the Broyden-Fletcher-Goldfarb-Shanno Algorithm on the Quasi-Newton Method
R code implementing BFGS Quasi-Newton Minimization Method
Pomodoro: Progressive Decomposition Methods with Acceleration
R function bfgs( ) implementing the 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.
A library for spectral line-shape analysis.
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