7 papers
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…
Heuristics for Combinatorial Optimization via Value-based Reinforcement Learning: A Unified Framework and Analysis
Orit Davidovich, Shimrit Shtern, Segev Wasserkrug +1
Since the 1990s, considerable empirical work has been carried out to train statistical models, such as neural networks (NNs), as learned heuristics for combinatorial optimization (…
Finding Probably Approximate Optimal Solutions by Training to Estimate the Optimal Values of Subproblems
Nimrod Megiddo, Segev Wasserkrug, Orit Davidovich +1
The paper is about developing a solver for maximizing a real-valued function of binary variables. The solver relies on an algorithm that estimates the optimal objective-function va…
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…