Important Markov-Chain Properties of (1,lambda)-ES Linear Optimization Models

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F14%3A00432405" target="_blank" >RIV/67985807:_____/14:00432405 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Important Markov-Chain Properties of (1,lambda)-ES Linear Optimization Models

Popis výsledku v původním jazyce

Several recent publications investigated Markov-chain modelling of linear optimization by a (1,lambda)-ES, considering both unconstrained and linearly constrained optimization, and both constant and varying step size. All of them assume normality of theinvolved random steps. This is a very strong and specific assumption. The objective of our contribution is to show that in the constant step size case, valuable properties of the Markov chain can be obtained even for steps with substantially more generaldistributions. Several results that have been previously proved using the normality assumption are proved here in a more general way without that assumption. Finally, the decomposition of a multidimensional distribution into its marginals and the copulacombining them is applied to the new distributional assumptions, particular attention being paid to distributions with Archimedean copulas.

Název v anglickém jazyce

Important Markov-Chain Properties of (1,lambda)-ES Linear Optimization Models

Popis výsledku anglicky

Several recent publications investigated Markov-chain modelling of linear optimization by a (1,lambda)-ES, considering both unconstrained and linearly constrained optimization, and both constant and varying step size. All of them assume normality of theinvolved random steps. This is a very strong and specific assumption. The objective of our contribution is to show that in the constant step size case, valuable properties of the Markov chain can be obtained even for steps with substantially more generaldistributions. Several results that have been previously proved using the normality assumption are proved here in a more general way without that assumption. Finally, the decomposition of a multidimensional distribution into its marginals and the copulacombining them is applied to the new distributional assumptions, particular attention being paid to distributions with Archimedean copulas.

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

<a href="/cs/project/GA13-17187S" target="_blank" >GA13-17187S: Konstrukce pokročilých srozumitelných klasifikátorů</a><br>

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2014

Kód důvěrnosti údajů

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

Název statě ve sborníku

ITAT 2014. Information Technologies - Applications and Theory. Part II

ISBN

978-80-87136-19-5

ISSN

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e-ISSN

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Počet stran výsledku

9

Strana od-do

44-52

Název nakladatele

Institute of Computer Science AS CR

Místo vydání

Prague

Místo konání akce

Demänovská dolina

Datum konání akce

25. 9. 2014

Typ akce podle státní příslušnosti

EUR - Evropská akce

Kód UT WoS článku

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