Approximate Bregman proximal gradient algorithm
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Updated
Dec 15, 2023 - Python
Approximate Bregman proximal gradient algorithm
An open-source MATLAB benchmark for reduced-precision solver verification
Optimization Algorithms for educational purposes.
Solver for minimization problems over the l1-ball
Bregman Proximal type algorithms
Implementation and brief comparison of different First Order and different Proximal gradient methods, comparison of their convergence rates
The SLTP Generalized Planning Framework: Sample, Learn, Transform & Plan
Frank--Wolfe algorithms for PDE-constrained optimization
Implementation and comparison of zero order vs first order method on the AdaMM (aka AMSGrad) optimizer: analysis of convergence rates and minima shape
Code to reproduce the results presented in the work "Efficient First-order Methods for Convex Minimization: a Constructive Approach" (in Mathematical Programming series A) by Y. Drori and A. Taylor.
Topics in Signal Processing
Suite of Predictive Controllers for Industrial Embedded Systems. A Matlab toolbox for automatic code generation of solvers for MPC controllers.
Demonstration of the PIPG algorithm for trajectory optimization
Mixed-Integer Convex Programming: Branch-and-bound with Frank-Wolfe-based convex relaxations
A Julia package that solves Linearly Constrained Separable Optimization Problems using ADMM.
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.
PEPit is a package enabling computer-assisted worst-case analyses of first-order optimization methods.
Julia implementation for various Frank-Wolfe and Conditional Gradient variants
Proximal operators for nonsmooth optimization in Julia
Splitting Conic Solver
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