КФУ. Карточка публикации. Adaptive Conditional Gradient Method

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ADAPTIVE CONDITIONAL GRADIENT METHOD

Форма представления

Статьи в зарубежных журналах и сборниках

Год публикации

2019

Язык

английский

Авторы, сотрудники КФУ

Габидуллина Зульфия Равилевна, автор

Библиографическое описание на языке оригинала

Gabidullina Z.R., Adaptive Conditional Gradient Method//Journal of Optimization Theory and Applications. - 2019. - Vol.183, Is.3. - P.1077-1098.

Аннотация

We present a novel fully adaptive conditional gradient method with the step length regulation for solving pseudo-convex constrained optimization problems. We propose some deterministic rules of the step length regulation in a normalized direction. These rules guarantee to find the step length by utilizing the finite procedures and provide the strict relaxation of the objective function at each iteration. We prove that the sequence of the function values for the iterates generated by the algorithm converges globally to the objective function optimal value with sublinear rate.

Ключевые слова

Optimization problems, Pseudo-convex function, Adaptation, Descent direction, Normalization, Step length Regulation, Rate of convergence.

Название журнала

Journal of Optimization Theory and Applications

URL

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85074016312&doi=10.1007%2fs10957-019-01585-w&partnerID=40&md5=61f69fdf521f5d9717001747d2b37c40

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https://repository.kpfu.ru/?p_id=215227

Полная запись метаданных

Поле DC	Значение	Язык
dc.contributor.author	Габидуллина Зульфия Равилевна	ru_RU
dc.date.accessioned	2019-01-01T00:00:00Z	ru_RU
dc.date.available	2019-01-01T00:00:00Z	ru_RU
dc.date.issued	2019	ru_RU
dc.identifier.citation	Gabidullina Z.R., Adaptive Conditional Gradient Method//Journal of Optimization Theory and Applications. - 2019. - Vol.183, Is.3. - P.1077-1098.	ru_RU
dc.identifier.uri	https://repository.kpfu.ru/?p_id=215227	ru_RU
dc.description.abstract	Journal of Optimization Theory and Applications	ru_RU
dc.description.abstract	We present a novel fully adaptive conditional gradient method with the step length regulation for solving pseudo-convex constrained optimization problems. We propose some deterministic rules of the step length regulation in a normalized direction. These rules guarantee to find the step length by utilizing the finite procedures and provide the strict relaxation of the objective function at each iteration. We prove that the sequence of the function values for the iterates generated by the algorithm converges globally to the objective function optimal value with sublinear rate.	ru_RU
dc.language.iso	ru	ru_RU
dc.subject	Optimization problems	ru_RU
dc.subject	Pseudo-convex function	ru_RU
dc.subject	Adaptation	ru_RU
dc.subject	Descent direction	ru_RU
dc.subject	Normalization	ru_RU
dc.subject	Step length Regulation	ru_RU
dc.subject	Rate of convergence.	ru_RU
dc.title	Adaptive Conditional Gradient Method	ru_RU
dc.type	Статьи в зарубежных журналах и сборниках	ru_RU