Linear optimization software

High-performance interior-point-method QP and QCQP solvers

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.

Clarabel.rs: Interior-point solver for convex conic optimisation problems in Rust.

With Uno, finally take full control of your SQP/barrier solver for nonlinearly constrained optimization

Efficient optimal control solvers for robotic systems.

Clarabel.jl: Interior-point solver for convex conic optimisation problems in Julia.

HPC solver for nonlinear optimization problems

Interior-point solver in pure Julia

qpSWIFT is a light-weight sparse quadratic programming solver

A Proximal Interior Point Quadratic Programming solver

C++ interface for hpipm, a high-performance interior point MPC solver

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

Reverse mode autodiff library, interior-point method, and NLP solver DSL for C++23 and Python

Clarabel.cpp: C/C++ interface to the Clarabel Interior-point solver for convex conic optimisation problems.

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.

Quadratic Objective Conic Optimizer

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

interior point method for linear programming

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

Documentation for the Clarabel interior point conic solver

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

C++ Implementation of MPPI-IPDDP (Model Predictive Path Integral - Interior Point Differential Dynamic Programming) and Testing with MPPI variants and Multiple Environments

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

Clarabel Solver for Java

Mehrotra's Predictor-Corrector Interior Point Method

No description provided.

Generic Solver (Primal Interior Point Method)

An Open Source Deep Learning Framework for Solving Inverse Optimization Problem

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