interior-point-method

Incremental Potential Contact (IPC) is for robust and accurate time stepping of nonlinear elastodynamics. IPC guarantees intersection- and inversion-free trajectories regardless of materials, time-step sizes, velocities, or deformation severity.

Linear optimization software

High-performance interior-point-method QP solvers

HPC solver for nonlinear optimization problems

Interior-point solver in pure Julia

Efficient optimal control solvers for robotic systems.

An interior-point method written in python for solving constrained and unconstrained nonlinear optimization problems.

qpSWIFT is a light-weight sparse quadratic programming solver

interior point method for linear programming

C++ implementation of the Interior Point Methods (CPPIPM)

This book offers a theoretical and computational presentation of a variety of linear programming algorithms and methods with an emphasis on the revised simplex method and its components. A theoretical background and mathematical formulation is included for each algorithm as well as comprehensive numerical examples and corresponding MATLAB® code. The MATLAB® implementations presented in this book are sophisticated and allow users to find solutions to large-scale benchmark linear programs. Each algorithm is followed by a computational study on benchmark problems that analyze the computational behavior of the presented algorithms. As a solid companion to existing algorithmic-specific literature, this book will be useful to researchers, scientists, mathematical programmers, and students with a basic knowledge of linear algebra and calculus. The clear presentation enables the reader to understand and utilize all components of simplex-type methods, such as presolve techniques, scaling techniques, pivoting rules, basis update methods, and sensitivity analysis.

A trust-region interior-point method for general nonlinear programing problems (GSoC 2017).

Mehrotra's Predictor-Corrector Interior Point Method

A Python implementation of Simplex and Interior-Point algorithms for solving Linear Programs (LPs)

An Open Source Deep Learning Framework for Solving Inverse Optimization Problem

Implementation of Interior Points Method in MATLAB (My Assignment in Linear Optimization course [MTH305] [IIIT-Delhi]).

A Matlab object-oriented implementation of infeasible primal-dual path-following interior point method.

Code of my qualification and PhD works: reconstruction and correction of 3D polyhedra from shadow contours

Homogeneous Self-Dual Method for Linear Programs

Generic Solver (Primal Interior Point Method)

playground for numerical optimization

Linear Optimisation Using Interior Point Method

A simple Linear Programming (LP) solver in Python

Code for symbolic validations of the PEP-based proofs for the article " Worst-case convergence analysis of gradient and Newton methods through semidefinite programming performance estimation" authored by E. de Klerk, F. Glineur and A. Taylor

DRIP Numerical Optimizer is a collection of Java libraries for Numerical Optimization and Spline Functionality.

Code written as a part of assignments for MTH374 - Linear Optimisation taken by Dr. Pravesh Biyani at IIIT Delhi in Winter 2019 Semester

Unofficial implementation of the paper "The Wasserstein Distance as a Dissimilarity Measure for Mass Spectra with Application to Spectral Deconvolution"

Mehrotra's Predictor-Corrector Algorithm for Linear Programming