Value prediction in engineering applications

G. Ziegler, Z. Palotai, T. Cinkler, P. Arató, A. Lőrincz

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In engineering application heuristics are widely used for discrete optimization tasks.We report two cases (in Dense Wavelength Division Multiplexing and High Level Synthesis), where a recent “intelligent” heuristic (STAGE) performs excellently by learning a value-function of the states. We have found that if a global structure of local minima is found by the function approximator then search time may not have to scale with the dimension of the problem in the exponent, but it may become a polynomial function of the dimension.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer Verlag
Pages25-34
Number of pages10
Volume2070
ISBN (Print)3540422196, 9783540422198
Publication statusPublished - 2001
Event14th International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, IEA/AIE 2001 - Budapest, Hungary
Duration: Jun 4 2001Jun 7 2001

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume2070
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other14th International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, IEA/AIE 2001
CountryHungary
CityBudapest
Period6/4/016/7/01

Fingerprint

Engineering Application
Heuristics
High-level Synthesis
Prediction
Discrete Optimization
Multiplexing
Polynomial function
Local Minima
Value Function
Division
Dense wavelength division multiplexing
Exponent
Wavelength
Polynomials
Learning

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Ziegler, G., Palotai, Z., Cinkler, T., Arató, P., & Lőrincz, A. (2001). Value prediction in engineering applications. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 2070, pp. 25-34). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2070). Springer Verlag.

Value prediction in engineering applications. / Ziegler, G.; Palotai, Z.; Cinkler, T.; Arató, P.; Lőrincz, A.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 2070 Springer Verlag, 2001. p. 25-34 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2070).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Ziegler, G, Palotai, Z, Cinkler, T, Arató, P & Lőrincz, A 2001, Value prediction in engineering applications. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). vol. 2070, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 2070, Springer Verlag, pp. 25-34, 14th International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, IEA/AIE 2001, Budapest, Hungary, 6/4/01.
Ziegler G, Palotai Z, Cinkler T, Arató P, Lőrincz A. Value prediction in engineering applications. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 2070. Springer Verlag. 2001. p. 25-34. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
Ziegler, G. ; Palotai, Z. ; Cinkler, T. ; Arató, P. ; Lőrincz, A. / Value prediction in engineering applications. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 2070 Springer Verlag, 2001. pp. 25-34 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
@inproceedings{55cdc9dfb3fa4785bc95e26ca488bd65,
title = "Value prediction in engineering applications",
abstract = "In engineering application heuristics are widely used for discrete optimization tasks.We report two cases (in Dense Wavelength Division Multiplexing and High Level Synthesis), where a recent “intelligent” heuristic (STAGE) performs excellently by learning a value-function of the states. We have found that if a global structure of local minima is found by the function approximator then search time may not have to scale with the dimension of the problem in the exponent, but it may become a polynomial function of the dimension.",
author = "G. Ziegler and Z. Palotai and T. Cinkler and P. Arat{\'o} and A. Lőrincz",
year = "2001",
language = "English",
isbn = "3540422196",
volume = "2070",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "25--34",
booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

}

TY - GEN

T1 - Value prediction in engineering applications

AU - Ziegler, G.

AU - Palotai, Z.

AU - Cinkler, T.

AU - Arató, P.

AU - Lőrincz, A.

PY - 2001

Y1 - 2001

N2 - In engineering application heuristics are widely used for discrete optimization tasks.We report two cases (in Dense Wavelength Division Multiplexing and High Level Synthesis), where a recent “intelligent” heuristic (STAGE) performs excellently by learning a value-function of the states. We have found that if a global structure of local minima is found by the function approximator then search time may not have to scale with the dimension of the problem in the exponent, but it may become a polynomial function of the dimension.

AB - In engineering application heuristics are widely used for discrete optimization tasks.We report two cases (in Dense Wavelength Division Multiplexing and High Level Synthesis), where a recent “intelligent” heuristic (STAGE) performs excellently by learning a value-function of the states. We have found that if a global structure of local minima is found by the function approximator then search time may not have to scale with the dimension of the problem in the exponent, but it may become a polynomial function of the dimension.

UR - http://www.scopus.com/inward/record.url?scp=84947551901&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84947551901&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:84947551901

SN - 3540422196

SN - 9783540422198

VL - 2070

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 25

EP - 34

BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

PB - Springer Verlag

ER -