The Parameterized Complexity of Cascading Portfolio Scheduling

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F19%3A00113727" target="_blank" >RIV/00216224:14330/19:00113727 - isvavai.cz</a>

Result on the web

<a href="http://papers.nips.cc/paper/8983-the-parameterized-complexity-of-cascading-portfolio-scheduling" target="_blank" >http://papers.nips.cc/paper/8983-the-parameterized-complexity-of-cascading-portfolio-scheduling</a>

DOI - Digital Object Identifier

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Result language

angličtina

Original language name

The Parameterized Complexity of Cascading Portfolio Scheduling

Original language description

Cascading portfolio scheduling is a static algorithm selection strategy which uses a sample of test instances to compute an optimal ordering (a cascading schedule) of a portfolio of available algorithms. The algorithms are then applied to each future instance according to this cascading schedule, until some algorithm in the schedule succeeds. Cascading algorithm scheduling has proven to be effective in several applications, including QBF solving and the generation of ImageNet classification models. It is known that the computation of an optimal cascading schedule in the offline phase is NP-hard. In this paper we study the parameterized complexity of this problem and establish its fixed-parameter tractability by utilizing structural properties of the success relation between algorithms and test instances. Our findings are significant as they reveal that in spite of the intractability of the problem in its general form, one can indeed exploit sparseness or density of the success relation to obtain non-trivial runtime guarantees for finding an optimal cascading schedule.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

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Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2019

Confidentiality

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

Article name in the collection

Advances in Neural Information Processing Systems 32 (NIPS 2019)

ISBN

9781728110448

ISSN

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e-ISSN

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Number of pages

11

Pages from-to

7666-7676

Publisher name

Neural Information Processing Systems Foundation, Inc.

Place of publication

USA

Event location

Canada

Event date

Jan 1, 2019

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

000534424307066