An optimal algorithm and superrelaxation for minimization of a quadratic function subject to separable convex constraints with applications

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F12%3A86085078" target="_blank" >RIV/61989100:27240/12:86085078 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/61989100:27740/12:86085078

Výsledek na webu

<a href="http://dx.doi.org/10.1007/s10107-011-0454-2" target="_blank" >http://dx.doi.org/10.1007/s10107-011-0454-2</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s10107-011-0454-2" target="_blank" >10.1007/s10107-011-0454-2</a>

Jazyk výsledku

angličtina

Název v původním jazyce

An optimal algorithm and superrelaxation for minimization of a quadratic function subject to separable convex constraints with applications

Popis výsledku v původním jazyce

We propose a modification of our MPGP algorithm for the solution of bound constrained quadratic programming problems so that it can be used for minimization of a strictly convex quadratic function subject to separable convex constraints. Our active set based algorithm explores the faces by conjugate gradients and changes the active sets and active variables by gradient projections, possibly with the superrelaxation steplength. The error estimate in terms of extreme eigenvalues guarantees that if a classof minimization problems has the spectrum of the Hessian matrix in a given positive interval, then the algorithm can find and recognize an approximate solution of any particular problem in a number of iterations that is uniformly bounded. We also show how to use the algorithm for the solution of separable and equality constraints. The power of our algorithm and its optimality are demonstrated on the solution of a problem of two cantilever beams in mutual contact with Tresca friction dis

Název v anglickém jazyce

An optimal algorithm and superrelaxation for minimization of a quadratic function subject to separable convex constraints with applications

Popis výsledku anglicky

We propose a modification of our MPGP algorithm for the solution of bound constrained quadratic programming problems so that it can be used for minimization of a strictly convex quadratic function subject to separable convex constraints. Our active set based algorithm explores the faces by conjugate gradients and changes the active sets and active variables by gradient projections, possibly with the superrelaxation steplength. The error estimate in terms of extreme eigenvalues guarantees that if a classof minimization problems has the spectrum of the Hessian matrix in a given positive interval, then the algorithm can find and recognize an approximate solution of any particular problem in a number of iterations that is uniformly bounded. We also show how to use the algorithm for the solution of separable and equality constraints. The power of our algorithm and its optimality are demonstrated on the solution of a problem of two cantilever beams in mutual contact with Tresca friction dis

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

BA - Obecná matematika

OECD FORD obor

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Projekt

—

Návaznosti

Z - Vyzkumny zamer (s odkazem do CEZ)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2012

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Mathematical Programming

ISSN

0025-5610

e-ISSN

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Svazek periodika

135

Číslo periodika v rámci svazku

1-2

Stát vydavatele periodika

DE - Spolková republika Německo

Počet stran výsledku

26

Strana od-do

195-220

Kód UT WoS článku

000308647100007

EID výsledku v databázi Scopus

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