34 citations · 34 across the 9 of their papers we have counts for
12 papers
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…
Reconstruction of Functions from Prescribed Proximal Points
Patrick L. Combettes, Zev C. Woodstock
Under investigation is the problem of finding the best approximation of a function in a Hilbert space subject to convex constraints and prescribed nonlinear transformations. We sho…
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…
Obstructions to convexity in neural codes
Caitlin Lienkaemper, Anne Shiu, Zev Woodstock
How does the brain encode spatial structure? One way is through hippocampal neurons called place cells, which become associated to convex regions of space known as their receptive…
Analyzing Multistationarity in Chemical Reaction Networks using the Determinant Optimization Method
Bryan Félix, Anne Shiu, Zev Woodstock
Multistationary chemical reaction networks are of interest to scientists and mathematicians alike. While some criteria for multistationarity exist, obtaining explicit reaction rate…