semidefinite-programming

COSMO: Accelerated ADMM-based solver for convex conic optimisation problems (LP, QP, SOCP, SDP, ExpCP, PowCP). Automatic chordal decomposition of sparse semidefinite programs.

optimization solver julia-language sdp semidefinite-programming convex-optimization conic-programs

UpdatedFeb 16, 2025
Julia

vprusso /toqito

Sponsor

Star257

|toqito> (Theory of Quantum Information Toolkit) is a Python library for research in quantum information theory.

python research physics quantum linear-algebra python3 quantum-computing linear python-3 quantum-information semidefinite-programming quantum-programming-language convex-optimization quantum-physics matrix-analysis quantum-information-science quantum-programming quantum-information-theory unitaryhack nonlocal-game

UpdatedJul 15, 2025
Python

oxfordcontrol /Clarabel.jl

Star210

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

optimization linear-programming julia-language semidefinite-programming optimization-algorithms quadratic-programming convex-optimization interior-point-method conic-programs conic-optimization

UpdatedJul 14, 2025
Julia

MIT-SPARK /CertifiablyRobustPerception

Star120

Certifiable Outlier-Robust Geometric Perception

computer-vision robotics autonomous-driving semidefinite-programming convex-optimization mixed-integer-programming point-cloud-registration robust-estimation outliers-detection

UpdatedDec 2, 2023
MATLAB

JuliaNonconvex /Nonconvex.jl

Star117

Toolbox for gradient-based and derivative-free non-convex constrained optimization with continuous and/or discrete variables.

optimization automatic-differentiation evolutionary-algorithms global-optimization black-box-optimization semidefinite-programming bayesian-optimization nonlinear-optimization nonlinear-programming metaheuristics discrete-optimization derivative-free-optimization method-of-moving-asymptotes implicit-differentiation gradient-based-optimisation augmented-lagrangian-method interior-point-optimizer surrogate-assisted-optimization mixed-integer-nonlinear-programming

UpdatedJan 23, 2024
Julia

WU-CVGL /GlobustVP

Star111

[CVPR 2025 Award Candidate & Oral] Convex Relaxation for Robust Vanishing Point Estimation in Manhattan World

optimization semidefinite-programming convex-optimization vanishing-points convex-relaxation

UpdatedJul 16, 2025
Python

baptistar /BOCS

Star97

Bayesian Optimization of Combinatorial Structures

simulated-annealing semidefinite-programming bayesian-optimization statistical-models

UpdatedOct 25, 2019
MATLAB

mariohsouto /ProxSDP.jl

Star95

Semidefinite programming optimization solver

optimization julia solver sdp semidefinite-programming convex-optimization conic-programs

UpdatedMay 25, 2025
Julia

PerformanceEstimation /PEPit

Star88

PEPit is a package enabling computer-assisted worst-case analyses of first-order optimization methods.

python optimization semidefinite-programming worst-case-analyses first-order-methods performance-estimation-problems

UpdatedJul 7, 2025
Python

arobey1 /LipSDP

Star69

LipSDP - Lipschitz Estimation for Neural Networks

neural-networks semidefinite-programming convex-optimization

UpdatedMar 7, 2022
MATLAB

MIT-SPARK /STRIDE

Star68

Solver for Large-Scale Rank-One Semidefinite Relaxations

semidefinite-programming convex-optimization polynomial-optimization large-scale-optimization semidefinite-relaxation

UpdatedOct 9, 2022
MATLAB

PerformanceEstimation /Performance-Estimation-Toolbox

Star54

Code of the Performance Estimation Toolbox (PESTO) whose aim is to ease the access to the PEP methodology for performing worst-case analyses of first-order methods in convex and nonconvex optimization. The numerical worst-case analyses from PEP can be performed just by writting the algorithms just as you would implement them.

optimization matlab semidefinite-programming optimization-algorithms worst-case-analyses first-order-methods performance-estimation-problems