Generalizations of the limited-memory BFGS method based on the quasi-product form of update

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F13%3A00381640" target="_blank" >RIV/67985807:_____/13:00381640 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1016/j.cam.2012.09.027" target="_blank" >http://dx.doi.org/10.1016/j.cam.2012.09.027</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.cam.2012.09.027" target="_blank" >10.1016/j.cam.2012.09.027</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Generalizations of the limited-memory BFGS method based on the quasi-product form of update

Popis výsledku v původním jazyce

Two families of limited-memory variable metric or quasi-Newton methods for unconstrained minimization based on quasi-product form of update are derived. As for the first family, four variants how to utilize the Strang recurrences for the Broyden class ofvariable metric updates are investigated; three of them use the same number of stored vectors as the limited- memory BFGS method. Moreover, one of the variants does not require any additional matrix by vector multiplication. The second family uses vectors from the preceding iteration to construct a new class of variable metric updates. Resulting methods again require neither any additional matrix by vector multiplication nor any additional stored vector. Global convergence of four of presented methodsis established for convex sufficiently smooth functions. Numerical results indicate that two of the new methods can save computational time substantially for certain problems.

Název v anglickém jazyce

Generalizations of the limited-memory BFGS method based on the quasi-product form of update

Popis výsledku anglicky

Two families of limited-memory variable metric or quasi-Newton methods for unconstrained minimization based on quasi-product form of update are derived. As for the first family, four variants how to utilize the Strang recurrences for the Broyden class ofvariable metric updates are investigated; three of them use the same number of stored vectors as the limited- memory BFGS method. Moreover, one of the variants does not require any additional matrix by vector multiplication. The second family uses vectors from the preceding iteration to construct a new class of variable metric updates. Resulting methods again require neither any additional matrix by vector multiplication nor any additional stored vector. Global convergence of four of presented methodsis established for convex sufficiently smooth functions. Numerical results indicate that two of the new methods can save computational time substantially for certain problems.

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

<a href="/cs/project/GA201%2F09%2F1957" target="_blank" >GA201/09/1957: Vývoj metod pro řešení rozsáhlých úloh nelineárního programování a nehladké optimalizace</a><br>

Návaznosti

Z - Vyzkumny zamer (s odkazem do CEZ)

Rok uplatnění

2013

Kód důvěrnosti údajů

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

Název periodika

Journal of Computational and Applied Mathematics

ISSN

0377-0427

e-ISSN

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Svazek periodika

241

Číslo periodika v rámci svazku

15 March

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

14

Strana od-do

116-129

Kód UT WoS článku

000312354100008

EID výsledku v databázi Scopus

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