34 citations · 34 across the 9 of their papers we have counts for
9 papers · 1 filter
Frank-Wolfe with Moreau Envelope Smoothing for Nonsmooth Nonconvex Problems
Antonio Silveti-Falls, Cesare Molinari, Zev Woodstock
We present and analyze Frank-Wolfe with Moreau Envelope Smoothing (FRAMES) for solving nonsmooth nonconvex constrained optimization problems, taking advantage of iterative smoothin…
A conditional-gradient-based single-loop augmented Lagrangian method for inequality constrained optimization
Xiaozhou Wang, Ting Kei Pong, Zev Woodstock
We consider the problem of minimizing the sum of a Lipschitz differentiable convex function and a proper closed convex function that admits efficient linear minimization or…
High-precision linear minimization is no slower than projection
Zev Woodstock
This note demonstrates that, for all compact convex sets, high-precision linear minimization can be performed via a single evaluation of the projection and a scalar-vector multipli…
Flexible block-iterative analysis for the Frank-Wolfe algorithm
Gábor Braun, Jannis Halbey, Sebastian Pokutta +1
We prove that the block-coordinate Frank-Wolfe (BCFW) algorithm converges with state-of-the-art rates in both convex and nonconvex settings under a very mild "block-iterative" assu…
Splitting the Conditional Gradient Algorithm
Zev Woodstock, Sebastian Pokutta
We propose a novel generalization of the conditional gradient (CG / Frank-Wolfe) algorithm for minimizing a smooth function under an intersection of compact convex sets, using…
On a Frank-Wolfe Approach for Abs-smooth Functions
Timo Kreimeier, Sebastian Pokutta, Andrea Walther +1
We propose an algorithm which appears to be the first bridge between the fields of conditional gradient methods and abs-smooth optimization. Our problem setting is motivated by var…
A Variational Inequality Model for the Construction of Signals from Inconsistent Nonlinear Equations
Patrick L. Combettes, Zev C. Woodstock
Building up on classical linear formulations, we posit that a broad class of problems in signal synthesis and in signal recovery are reducible to the basic task of finding a point…
Block-Activated Algorithms for Multicomponent Fully Nonsmooth Minimization
Minh N. Bùi, Patrick L. Combettes, Zev C. Woodstock
Under consideration are multicomponent minimization problems involving a separable nonsmooth convex function penalizing the components individually, and nonsmooth convex coupling t…
A Fixed Point Framework for Recovering Signals from Nonlinear Transformations
Patrick L. Combettes, Zev. C. Woodstock
We consider the problem of recovering a signal from nonlinear transformations, under convex constraints modeling a priori information. Standard feasibility and optimization methods…