A note on the convergence of an inertial version of a diagonal hybrid projection-point algorithm

Miguel Carrasco; Karine Pichard

doi:10.1080/02331930801951389

A note on the convergence of an inertial version of a diagonal hybrid projection-point algorithm

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Abstract

In this note, an inertial and relaxed version of a diagonal hybrid projectionproximal point algorithm is considered, in order to find the minimum of a function f approximated by a sequence of functions (in general, smoother than f or taking into account some constraints of the problem). Two convergence theorems are proved under different kind of assumptions, which allows to apply the method in various cases.

Original language	American English
Pages (from-to)	561-574
Number of pages	14
Journal	Optimization
Volume	59
Issue number	4
DOIs	https://doi.org/10.1080/02331930801951389
State	Published - 1 Jan 2010

Keywords

Diagonal iteration
Global convergence
Hybrid method
Inertial term
Parametric approximation
Proximal point
Relaxation

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AU - Pichard, Karine

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AB - In this note, an inertial and relaxed version of a diagonal hybrid projectionproximal point algorithm is considered, in order to find the minimum of a function f approximated by a sequence of functions (in general, smoother than f or taking into account some constraints of the problem). Two convergence theorems are proved under different kind of assumptions, which allows to apply the method in various cases.

KW - Diagonal iteration

KW - Global convergence

KW - Hybrid method

KW - Inertial term

KW - Parametric approximation

KW - Proximal point

KW - Relaxation

KW - Diagonal iteration

KW - Global convergence

KW - Hybrid method

KW - Inertial term

KW - Parametric approximation

KW - Proximal point

KW - Relaxation

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JF - Optimization

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A note on the convergence of an inertial version of a diagonal hybrid projection-point algorithm

Abstract

Keywords

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