This repository contains several Frank-Wolfe (a.k.a. Conditional Gradient) algorithms implemented in Python. A sister repository implemented in Julia can be found here.

Nov 25, 2022 · The purpose of this survey is to serve both as a gentle introduction and a coherent overview of state-of-the-art Frank--Wolfe algorithms, also called conditional gradient …

We show a general method to lazify various conditional gradient algorithms, which in actual computations leads to several orders of magnitude of speedup in wall-clock time. This is …

Herein we describe the conditional-gradient method for solving P, also called the Frank-Wolfe method. This method is one of the cornerstones of opti-mization, and was one of the first …

Jul 30, 2021 · A very versatile and simple optimization method for projection-free optimization that promotes sparsity. Why? Constraints and Sparsity help interpretability and explainability. …

We show that under convexity and smoothness assumptions, our proposed stochastic conditional gradient method converges to the optimal objective function value at a sublinear rate of …

First, asymptotic analysis to multiobjective conditional gradient method will be done for Armijo and adaptative step sizes. Second, iteration-complexity bounds will be stablished for Armijo, …

Nov 15, 2021 · Conditional gradient, convex commination algorithm I For problem we can solve by FW algorithm, what is the alternative method? Projected gradient descent (PGD).

In this work, we describe a blended conditional gradient (BCG) approach, which takes one of several types of steps on the basis of the gradient rf at the current point. Our ap-proach …

We propose a novel generalization of the conditional gradient (CG / Frank-Wolfe) algorithm for minimizing a smooth function f under an intersection of compact convex sets, using a rst-order …