10 citations · 10 across the 2 of their papers we have counts for
7 papers · 1 filter
First-Order Methods for Convex Optimization
Pavel Dvurechensky, Mathias Staudigl, Shimrit Shtern
First-order methods for solving convex optimization problems have been at the forefront of mathematical optimization in the last 20 years. The rapid development of this important c…
Generalized Self-Concordant Analysis of Frank-Wolfe algorithms
Pavel Dvurechensky, Kamil Safin, Shimrit Shtern +1
Projection-free optimization via different variants of the Frank-Wolfe (FW) method has become one of the cornerstones in large scale optimization for machine learning and computati…
Self-Concordant Analysis of Frank-Wolfe Algorithms
Pavel Dvurechensky, Petr Ostroukhov, Kamil Safin +2
Projection-free optimization via different variants of the Frank-Wolfe (FW), a.k.a. Conditional Gradient method has become one of the cornerstones in optimization for machine learn…
Two-stage sample robust optimization
Dimitris Bertsimas, Shimrit Shtern, Bradley Sturt
We investigate a simple approximation scheme, based on overlapping linear decision rules, for solving data-driven two-stage distributionally robust optimization problems with the t…
A Scalable Algorithm for Two-Stage Adaptive Linear Optimization
Dimitris Bertsimas, Shimrit Shtern
The column-and-constraint generation (CCG) method was introduced by \citet{Zeng2013} for solving two-stage adaptive optimization. We found that the CCG method is quite scalable, bu…
A First Order Method for Solving Convex Bi-Level Optimization Problems
Shoham Sabach, Shimrit Shtern
In this paper we study convex bi-level optimization problems for which the inner level consists of minimization of the sum of smooth and nonsmooth functions. The outer level aims a…