Julia implementation for various Frank-Wolfe and Conditional Gradient variants
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Updated
Sep 10, 2024 - Julia
Julia implementation for various Frank-Wolfe and Conditional Gradient variants
Mixed-Integer Convex Programming: Branch-and-bound with Frank-Wolfe-based convex relaxations
Splitting Conic Solver
PEPit is a package enabling computer-assisted worst-case analyses of first-order optimization methods.
Suite of Predictive Controllers for Industrial Embedded Systems. A Matlab toolbox for automatic code generation of solvers for MPC controllers.
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.
Frank--Wolfe algorithms for PDE-constrained optimization
Approximate Bregman proximal gradient algorithm
Proximal operators for nonsmooth optimization in Julia
Bregman Proximal type algorithms
Implementation and comparison of zero order vs first order method on the AdaMM (aka AMSGrad) optimizer: analysis of convergence rates and minima shape
Implementation and brief comparison of different First Order and different Proximal gradient methods, comparison of their convergence rates
Topics in Signal Processing
Optimization Algorithms for educational purposes.
Demonstration of the PIPG algorithm for trajectory optimization
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.
An open-source MATLAB benchmark for reduced-precision solver verification
Solver for minimization problems over the l1-ball
A Julia package that solves Linearly Constrained Separable Optimization Problems using ADMM.
The SLTP Generalized Planning Framework: Sample, Learn, Transform & Plan
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