Titlebook: Mathematical Optimization Theory and Operations Research; 19th International C Yury Kochetov,Igor Bykadorov,Tatiana Gruzdeva Conference pro

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Sensitive Instances of the Cutting Stock Probleml. We adapt our method presented in[.] to search for sensitive instances with required properties and construct a CSP instance with gap .. We also present several instances with large gaps much smaller than previously known.

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On Multiple Coverings of Fixed Size Containers with Non-Euclidean Metric by Circles of Two Types based on an optical-geometric approach, which is developed by the authors in recent years and previously used only for circles of an equal radius. The results of a computational experiment are presented and discussed.

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Analogues of Switching Subgradient Schemes for Relatively Lipschitz-Continuous Convex Programming Prtive function and functional constraints. Some class of online convex optimization problems is considered. The proposed methods are optimal in the class of optimization problems with relatively Lipschitz-continuous objective and functional constraints.

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Conference proceedings 2020ollowing topical headings: ​combinatorial optimization; mathematical programming; global optimization; game theory and mathematical economics; heuristics and metaheuristics; machine learning and data analysis..

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One Problem of the Sum of Weighted Convolution Differences Minimization, Induced by the Quasiperiodince . from the given alphabet..We have constructed the algorithm which finds the exact solution to this problem in polynomial time. The numerical simulation demonstrates that this algorithm can be used to solve modeled applied problems of noise-proof processing of quasiperiodic signals.

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Gradient-Free Methods with Inexact Oracle for Convex-Concave Stochastic Saddle-Point Problemcified not only on the optimization set itself, but in a certain neighbourhood of it. In the second part of the paper, we analyze the case when such an assumption cannot be made, we propose a general approach on how to modernize the method to solve this problem, and also we apply this approach to particular cases ofsomeclassical sets.

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Near-Optimal Hyperfast Second-Order Method for Convex Optimizationts convergence and merge it with the scheme of solving subproblem using only the second-order oracle. As a result, we get convergence rate . up to a logarithmic factor. This convergence rate is near-optimal and the best known up to this moment.

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1865-0929 MOTOR 2020, held in Novosibirsk, Russia, in July 2020. Due to the COVID-19 pandemic the conference was held online.   .The 25 full papers and 8 short papers presented in this volume were carefully reviewed and selected from a total of 102 submissions. The papers in the volume are organised accordin