A parallelized, arbitrary precision semidefinite program solver based on the primal-dual interior-point method.
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Updated
Feb 3, 2024 - Julia
A parallelized, arbitrary precision semidefinite program solver based on the primal-dual interior-point method.
A custom branch-and-bound algorithm for solving low-rank matrix completion to certifiable optimality
Julia implementation of the Augmented Mixing Method for semidefinite programs (SDPs)
Implementation of the Homotopy method for convex optimization problems introduced in https://arxiv.org/abs/2403.02095.
Reformulation of SDPs using block factor-width two matrices
This is the Python (Manim) code for a video I did on the Goemans-Williamson Semidefinite-Programming optimization to the max cut problem. Inspired by this project:
Implementation of an interior-point algorithm for linear and semidefinite optimization
Matrix-Valued Systems Toolbox (MVST): MATLAB toolbox for linear matrix-valued (impulsive and stochastic) dynamical systems: mean-square stability, stabilization, optimal control, covariance steering, dissipativity, KYP, H-infinity, games, positive systems. SOS/SDP engines with bundled solvers. Companion to a Springer monograph in preparation.
Parser for CVXR to solve the Gaussian MLE problem with added constraints.
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