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An Inertial Proximal-Gradient Penalization Scheme for Constrained Convex Optimization Problems

نظام معاقبة بالقصور الذاتي بالتدرج الداني لمشاكل التحسين المحدبة المقيدة
Authors: Radu Ioan Boţ; Ernö Robert Csetnek; Nimit Nimana;

An Inertial Proximal-Gradient Penalization Scheme for Constrained Convex Optimization Problems

Abstract

Nous proposons un algorithme de gradient proximal avec des termes de pénalisation et des effets inertiels et de mémoire pour minimiser la somme d'une fonction semi-continue propre, convexe et inférieure et d'une fonction différentiable convexe soumise à l'ensemble des minimiseurs d'une autre fonction différentiable convexe. Nous montrons que, sous des choix appropriés pour les tailles de pas et les paramètres de pénalisation, les itérations générées convergent faiblement vers une solution optimale du problème d'optimisation bilevel adressé, tandis que les valeurs de fonction objective convergent vers sa valeur objective optimale.

Proponemos un algoritmo de gradiente proximal con términos de penalización y efectos inerciales y de memoria para minimizar la suma de una función diferenciable adecuada, convexa e inferior y una función diferenciable convexa sujeta al conjunto de minimizadores de otra función diferenciable convexa. Mostramos que, bajo elecciones adecuadas para los tamaños de paso y los parámetros de penalización, las iteraciones generadas convergen débilmente hacia una solución óptima del problema de optimización binivel abordado, mientras que los valores de la función objetivo convergen hacia su valor objetivo óptimo.

We propose a proximal-gradient algorithm with penalization terms and inertial and memory effects for minimizing the sum of a proper, convex, and lower semicontinuous and a convex differentiable function subject to the set of minimizers of another convex differentiable function. We show that, under suitable choices for the step sizes and the penalization parameters, the generated iterates weakly converge to an optimal solution of the addressed bilevel optimization problem, while the objective function values converge to its optimal objective value.

نقترح خوارزمية التدرج القريب مع مصطلحات المعاقبة وتأثيرات القصور الذاتي والذاكرة لتقليل مجموع وظيفة مناسبة ومحدبة ومنخفضة شبه مستمرة ومحدبة قابلة للاختلاف تخضع لمجموعة من أدوات التقليل من وظيفة أخرى محدبة قابلة للاختلاف. نوضح أنه في ظل الخيارات المناسبة لأحجام الخطوات ومعلمات المعاقبة، تتقارب التكرارات المتولدة بشكل ضعيف إلى حل أمثل لمشكلة تحسين المستوى الثنائي التي تم تناولها، بينما تتقارب قيم الدالة الموضوعية إلى قيمتها الموضوعية المثلى.

Country
Austria
Keywords

Optimization, Convex programming, penalization, Convex Optimization, Bilevel Programming, 101016 Optimisation, Proximal-gradient algorithm, Computational Mechanics, Geometry, 101002 Analysis, Evolutionary biology, Proximal Gradient Methods, Mathematical analysis, Quantum mechanics, Article, Inertial algorithm, Proper convex function, Engineering, Numerical mathematical programming methods, Differentiable function, Convex function, FOS: Mathematics, proximal-gradient algorithm, Biology, Numerical Analysis, Global Optimization, Numerical Optimization Techniques, Optimization Software, Physics, Mathematical optimization, Iterative Algorithms for Nonlinear Operators and Optimization, Theory and Applications of Compressed Sensing, Applied mathematics, Convex optimization, Regular polygon, Computational Theory and Mathematics, Function (biology), Fenchel conjugate, Computer Science, Physical Sciences, Inertial frame of reference, 101016 Optimierung, inertial algorithm, Iterated function, Subderivative, Penalization, Mathematics

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
10
Average
Average
Top 10%
Green
hybrid