A large scale non-linear optimization library

A header-only C++ library for L-BFGS and L-BFGS-B algorithms

LBFGS-Lite: A header-only L-BFGS unconstrained optimizer.

OptimKit: A blissfully ignorant Julia package for gradient optimization

C++ L-BFGS implementation using plain STL

Adjoint-state based AVO Inversion Method

A minimal Implementation of VGG16 Deep Learning Model in Python using L-BFGS to perform Image Styling/Blending

Large-scale Bound-constrained Optimization

R Package for Unconstrained Numerical Optimization

Python machine learning library using powerful numerical optimization methods.

Stochastic Quasi-Newton Methods in a Trust Region Framework (MATLAB implementation)

Implementation of a Neural Network with L-BFGS with Line Search and Gradient Descent with Momentum for numerical optimization purposes

Lightning-Fast Template-free Protein Folding based on Predicted Residue Contacts and Secondary Structure

Privacy-preserving survival analysis using multiparty computation

Color Generation Library

Canonical optimization for MIMO MAC design

This repo contains the codes for the project in UGP - II (CS 396)

A comparative optimization study of logistic regression for fake news detection, evaluating gradient descent, conjugate gradient, Newton, and L-BFGS methods on large-scale TF-IDF text features for convergence behavior, runtime, and memory efficiency.

My undergraduate research project with John D. Carter that implements the nontrivial time-periodic solution computer for the Whitham equation. Algorithm provided by David Ambrose & Jon Wilkening (2010).

Advanced Mathematical Optimization & Deep Learning Optimizers from scratch. Covers KKT duality, L-BFGS, proximal methods (ADMM, FISTA), stochastic algorithms (SVRG, Lion), and cutting-edge deep learning optimizers like K-FAC, Shampoo, Sophia, SAM, and Muon. Bridging strict convex calculus with large-scale Transformer training.