output
19932026
most citedOn space of integrable quantum field theories

664 citations

Showing 2018 · math.OCShow all

8 papers · 2 filters

math.OC2018

A Multidimensional Fatou Lemma for Conditional Expectations

E. Babaei, I. V. Evstigneev, K. R. Schenk-Hoppé

The classical multidimensional version of Fatou's lemma (Schmeidler 1970) originally obtained for unconditional expectations and the standard non-negative cone in a finite-dimensio…

math.OC2018

Packing of Circles on Square Flat Torus as Global Optimization of Mixed Integer Nonlinear problem

Sergey A. Smirnov, Vladimir V. Voloshinov

The article demonstrates rather general approach to problems of discrete geometry: treat them as global optimization problems to be solved by one of general purpose solver implemen…

math.OC2018★ 1 cited

Walrasian Equilibrium and Centralized Distributed Optimization from the point of view of Modern Convex Optimization Methods on the Example of Resource Allocation Problem

Anastasiya Ivanova, Alexander Gasnikov, Evgeni Nurminski +1

We consider the resource allocation problem and its numerical solution. The following constructions are demonstrated: 1) Walrasian price-adjustment mechanism for determining the eq…

math.OC2018★ 15 cited

Generalized Mirror Prox for Monotone Variational Inequalities: Universality and Inexact Oracle

Fedor Stonyakin, Alexander Gasnikov, Pavel Dvurechensky +2

We introduce an inexact oracle model for variational inequalities (VI) with monotone operator, propose a numerical method which solves such VI's and analyze its convergence rate. A…

math.OC2018★ 18 cited

Optimal Distributed Optimization on Slowly Time-Varying Graphs

Alexander Rogozin, César A. Uribe, Alexander Gasnikov +2

We study optimal distributed first-order optimization algorithms when the network (i.e., communication constraints between the agents) changes with time. This problem is motivated…

math.OC2018★ 30 cited

An Accelerated Directional Derivative Method for Smooth Stochastic Convex Optimization

Pavel Dvurechensky, Eduard Gorbunov, Alexander Gasnikov

We consider smooth stochastic convex optimization problems in the context of algorithms which are based on directional derivatives of the objective function. This context can be co…