Active set expansion strategies in MPRGP algorithm

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68145535%3A_____%2F20%3A00534448" target="_blank" >RIV/68145535:_____/20:00534448 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/61989100:27120/20:10245693 RIV/61989100:27240/20:10245693 RIV/61989100:27730/20:10245693

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/pii/S0965997819311627?via%3Dihub" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0965997819311627?via%3Dihub</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.advengsoft.2020.102895" target="_blank" >10.1016/j.advengsoft.2020.102895</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Active set expansion strategies in MPRGP algorithm

Popis výsledku v původním jazyce

The paper investigates strategies for expansion of active set that can be employed by the MPRGP algorithm. The standard MPRGP expansion uses a projected line search in the free gradient direction with a fixed step length. Such a scheme is often too slow to identify the active set, requiring a large number of expansions. We propose to use adaptive step lengths based on the current gradient, which guarantees the decrease of the unconstrained cost function with different gradient-based search directions. Moreover, we also propose expanding the active set by projecting the optimal step for the unconstrained minimization. Numerical experiments demonstrate the benefits (up to 78% decrease in the number of Hessian multiplications) of our expansion step modifications on two benchmarks – contact problem of linear elasticity solved by TFETI and machine learning problems of SVM type, both implemented in PERMON toolbox.

Název v anglickém jazyce

Active set expansion strategies in MPRGP algorithm

Popis výsledku anglicky

The paper investigates strategies for expansion of active set that can be employed by the MPRGP algorithm. The standard MPRGP expansion uses a projected line search in the free gradient direction with a fixed step length. Such a scheme is often too slow to identify the active set, requiring a large number of expansions. We propose to use adaptive step lengths based on the current gradient, which guarantees the decrease of the unconstrained cost function with different gradient-based search directions. Moreover, we also propose expanding the active set by projecting the optimal step for the unconstrained minimization. Numerical experiments demonstrate the benefits (up to 78% decrease in the number of Hessian multiplications) of our expansion step modifications on two benchmarks – contact problem of linear elasticity solved by TFETI and machine learning problems of SVM type, both implemented in PERMON toolbox.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10102 - Applied mathematics

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2020

Kód důvěrnosti údajů

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

Název periodika

Advances in Engineering Software

ISSN

0965-9978

e-ISSN

—

Svazek periodika

149

Číslo periodika v rámci svazku

November 2020

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

16

Strana od-do

102895

Kód UT WoS článku

000577084300005

EID výsledku v databázi Scopus

2-s2.0-85089702345