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Computational Study of Local Search Methods for a D.C. Optimization Problem with Inequality Constraints

Авторы: Barkova M.V., Strekalovskiy A.S.

Журнал: Lecture Notes in Computer Science: 12th International Conference Optimization and Applications (OPTIMA 2021, Petrovac, Montenegro, September 27 – October 1, 2021)

Том: 13078

Номер:

Год: 2021

Отчётный год: 2021

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Местоположение издательства:

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DOI: 10.1007/978-3-030-91059-4_7

Аннотация: This paper addresses a nonconvex optimization problem where the cost function and inequality constraints are d.c. functions. Two special local search methods based on the idea of the consecutive solution of partially linearized problems are developed. The latter problems turn out to be convex and therefore solvable with the help of software packages for convex optimization. The first method linearizes both the objective function of the problem and all constraints functions. The second approach is based on the reduction of the original problem to a penalized problem without constraints via the exact penalization theory. The methods developed were computationally tested on some well-known test examples and specially generated problems with known local and global solutions.

Индексируется WOS: Q4

Индексируется Scopus: Нет

Индексируется УБС: Нет

Индексируется РИНЦ: Да

Индексируется ВАК: Нет

Индексируется CORE: Нет

Публикация в печати: 0