Modeling language for Mathematical Optimization (linear, mixed-integer, conic, semidefinite, nonlinear)
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Updated
Mar 7, 2023 - Julia
Modeling language for Mathematical Optimization (linear, mixed-integer, conic, semidefinite, nonlinear)
Splitting Conic Solver
An implementation of the SE-Sync algorithm for synchronization over the special Euclidean group.
COSMO: Accelerated ADMM-based solver for convex conic optimisation problems (LP, QP, SOCP, SDP, ExpCP, PowCP). Automatic chordal decomposition of sparse semidefinite programs.
Clarabel.jl: Interior-point solver for convex conic optimisation problems in Julia.
Bayesian Optimization of Combinatorial Structures
Certifiable Outlier-Robust Geometric Perception
Semidefinite programming optimization solver
Clarabel.rs: Interior-point solver for convex conic optimisation problems in Rust.
PEPit is a package enabling computer-assisted worst-case analyses of first-order optimization methods.
Solver for Large-Scale Rank-One Semidefinite Relaxations
LipSDP - Lipschitz Estimation for Neural Networks
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.
A Julia/JuMP Package for Maximizing Algebraic Connectivity of Undirected Weighted Graphs
Polynomial optimization problem solver. Uses relaxation to convert the problem into Semidefinite programming. Can be also used just as Semidefinite programming solver.
Python Tools to Practically Model and Solve the Problem of High Speed Rotor Balancing.
MICO: Mutual Information and Conic Optimization for feature selection
Irene is a python package that aims to be a toolkit for global optimization problems that can be realized algebraically. It generalizes Lasserre's Relaxation method to handle theoretically any optimization problem with bounded feasibility set. The method is based on solutions of generalized truncated moment problems over commutative real algebras.
A Julia package for the computation of hard, theoretically guaranteed bounds on the moments of jump-diffusion processes with polynomial data
A Coq tactic for proving multivariate inequalities using SDP solvers
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