27 citations · 57 across the 8 of their papers we have counts for
9 papers
Dynamic FISTA for Convex Composite Bi-Level Optimization
Roey Merchav, Shoham Sabach, Marc Teboulle
In this paper, we study convex bi-level optimization problems where both the inner and outer levels are given as a composite convex minimization. We propose the Fast Bi-level Proxi…
Faster Lagrangian-Based Methods in Convex Optimization
Shoham Sabach, Marc Teboulle
In this paper, we aim at unifying, simplifying and improving the convergence rate analysis of Lagrangian-based methods for convex optimization problems. We first introduce the noti…
Optimization on Spheres: Models and Proximal Algorithms with Computational Performance Comparisons
D. Russell Luke, Shoham Sabach, Marc Teboulle
We present a unified treatment of the abstract problem of finding the best approximation between a cone and spheres in the image of affine transformations. Prominent instances of t…
Nonconvex Lagrangian-Based Optimization: Monitoring Schemes and Global Convergence
Jérôme Bolte, Shoham Sabach, Marc Teboulle
We introduce a novel approach addressing global analysis of a difficult class of nonconvex-nonsmooth optimization problems within the important framework of Lagrangian-based method…
First Order Methods beyond Convexity and Lipschitz Gradient Continuity with Applications to Quadratic Inverse Problems
Jérôme Bolte, Shoham Sabach, Marc Teboulle +1
We focus on nonconvex and nonsmooth minimization problems with a composite objective, where the differentiable part of the objective is freed from the usual and restrictive global…
Necessary conditions for linear convergence of iterated expansive, set-valued mappings with application to alternating projections
D. Russell Luke, Marc Teboulle, Nguyen H. Thao
We present necessary conditions for monotonicity, in one form or another, of fixed point iterations of mappings that violate the usual nonexpansive property. We show that most reas…
An optimal variant of Kelley's cutting-plane method
Yoel Drori, Marc Teboulle
We propose a new variant of Kelley's cutting-plane method for minimizing a nonsmooth convex Lipschitz-continuous function over the Euclidean space. We derive the method through a c…
Performance of first-order methods for smooth convex minimization: a novel approach
Yoel Drori, Marc Teboulle
We introduce a novel approach for analyzing the performance of first-order black-box optimization methods. We focus on smooth unconstrained convex minimization over the Euclidean s…
Conditional Gradient Algorithms for Rank-One Matrix Approximations with a Sparsity Constraint
Ronny Luss, Marc Teboulle
The sparsity constrained rank-one matrix approximation problem is a difficult mathematical optimization problem which arises in a wide array of useful applications in engineering,…