7 papers · 1 filter
Stochastic variance reduced extragradient methods for solving hierarchical variational inequalities
Pavel Dvurechensky, Andrea Ebner, Johannes Carl Schnebel +2
We are concerned with optimization in a broad sense through the lens of solving variational inequalities (VIs) -- a class of problems that are so general that they cover as particu…
Extragradient methods with complexity guarantees for hierarchical variational inequalities
Pavel Dvurechensky, Meggie Marschner, Shimrit Shtern +1
In the framework of a real Hilbert space we consider the problem of approaching solutions to a class of hierarchical variational inequality problems, subsuming several other proble…
Smooth Uncertainty Sets: Dependence of Uncertain Parameters via a Simple Polyhedral Set
Noam Goldberg, Michael Poss, Shimrit Shtern
We propose a novel polyhedral uncertainty set for robust optimization, termed the smooth uncertainty set, which captures dependencies of uncertain parameters by constraining their…
On the Convergence Rates of Iterative Regularization Algorithms for Composite Bi-Level Optimization
Shimrit Shtern, Adeolu Taiwo
This paper investigates iterative methods for solving bi-level optimization problems where both inner and outer functions have a composite structure. We establish novel theoretical…
A conditional gradient homotopy method with applications to Semidefinite Programming
Pavel Dvurechensky, Gabriele Iommazzo, Shimrit Shtern +1
We propose a new homotopy-based conditional gradient method for solving convex optimization problems with a large number of simple conic constraints. Instances of this template nat…
First-order algorithms for robust optimization problems via convex-concave saddle-point Lagrangian reformulation
Krzysztof Postek, Shimrit Shtern
Robust optimization (RO) is one of the key paradigms for solving optimization problems affected by uncertainty. Two principal approaches for RO, the robust counterpart method and t…