Titlebook: Mathematical Optimization Theory and Operations Research: Recent Trends; 20th International C Alexander Strekalovsky,Yury Kochetov,Andrei O

euphoria

天文臺(tái)

Zeroth-Order Algorithms for Smooth Saddle-Point Problemsirect product of two simplices, our convergence rate for the stochastic term is only by a . factor worse than for the first-order methods. Finally, we demonstrate the practical performance of our zeroth-order methods on practical problems.

musicologist

Algorithms for Solving Variational Inequalities and Saddle Point Problems with Some Generalizations s of the article is introducing an analogue of the boundedness condition for the operator in the case of arbitrary (not necessarily Euclidean) prox structure. We propose an analogue of the Mirror Descent method for solving variational inequalities with such operators, which is optimal in the considered class of problems.

maverick

Maximizing the Minimum Processor Load with Linear Externalitiescessors, i.e. .. For the case of two processors in this model the Nash equilibrium existence is proven and the expression for the Price of Anarchy is obtained. Also we show that the Price of Anarchy is limited in contrast to the initial model without externalities.

Glutinous

Analysis of Optimal Solutions to the Problem of a Single Machine with Preemption up with an algorithm for preprocessing the input data, which makes it possible to reduce the problem to a narrower class of examples. We also propose an approach to solving the special case of the problem with job durations two.

archetype

驚呼

An Acceleration of Decentralized SGD Under General Assumptions with Low Stochastic Noisenifying several centralized and decentralized approaches to stochastic distributed optimization was developed in Koloskova et al. (2020). In this work, we employ a Catalyst framework and accelerate the rates of Koloskova et al. (2020) in the case of low stochastic noise.

考古學(xué)

黃瓜

NP-Hardness of 1-Mean and 1-Medoid 2-Clustering Problem with Arbitrary Clusters Sizes while the center of the second cluster should be chosen from the set of the initial points (medoid). It is known that this problem is NP-hard if the cardinalities of the clusters are given as a part of the input. In this paper we prove that the peoblem remains NP-hard in the case of arbitrary clusters sizes.

acrophobia

Conference proceedings 2021ted in this volume were carefully reviewed and selected from a total of 102 submissions. The papers in the volume are organised according to the following topical headings: continuous optimization; integer programming and combinatorial optimization; operational research applications; optimal control..