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Found 5 papers, 2 papers with code
Self-concordant analysis of Frank-Wolfe algorithm
Projection-free optimization via different variants of the Frank-Wolfe (FW) method has become one of the cornerstones in optimization for machine learning since in many cases the linear minimization oracle is much cheaper to implement than projections and some sparsity needs to be preserved.
A conditional gradient homotopy method with applications to Semidefinite Programming
We propose a new homotopy-based conditional gradient method for solving convex optimization problems with a large number of simple conic constraints.
First-order algorithms for robust optimization problems via convex-concave saddle-point Lagrangian reformulation
Robust optimization (RO) is one of the key paradigms for solving optimization problems affected by uncertainty.
Optimization and Control
First-Order Methods for Convex Optimization
In this survey we cover a number of key developments in gradient-based optimization methods.
Self-Concordant Analysis of Frank-Wolfe Algorithms
Projection-free optimization via different variants of the Frank-Wolfe (FW), a. k. a.
