Warm start by Hopfield neural networks for interior point methods

Hopfield neural networks and interior point methods are used in an integrated way to solve linear optimization problems. The Hopfield network gives warm start for the primal–dual interior point methods, which can be way ahead in the path to optimality. The approaches were applied to a set of real wo...

Full description

Saved in:
Bibliographic Details
Published in:Computers & operations research 2007-09, Vol.34 (9), p.2553-2561
Main Authors: Fontova, Marta I. Velazco, Oliveira, Aurelio R.L., Lyra, Christiano
Format: Article
Language:English
Subjects:
Applied sciences
Artificial intelligence
Computer science
control theory
systems
Connectionism. Neural networks
Exact sciences and technology
Hopfield networks
Linear programming
Neural networks
Operational research. Management science
Optimization techniques
Primal–dual interior point methods
Problem solving
Studies
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by cdi_FETCH-LOGICAL-c385t-d89bd925f60a74dba492e7ff158ede8aaaaa4ebf9a702c7d0f64a6a518a137a33
cites cdi_FETCH-LOGICAL-c385t-d89bd925f60a74dba492e7ff158ede8aaaaa4ebf9a702c7d0f64a6a518a137a33
container_end_page 2561
container_issue 9
container_start_page 2553
container_title Computers & operations research
container_volume 34
creator Fontova, Marta I. Velazco
Oliveira, Aurelio R.L.
Lyra, Christiano
description Hopfield neural networks and interior point methods are used in an integrated way to solve linear optimization problems. The Hopfield network gives warm start for the primal–dual interior point methods, which can be way ahead in the path to optimality. The approaches were applied to a set of real world linear programming problems. The integrated approaches provide promising results, indicating that there may be a place for neural networks in the “real game” of optimization.
doi_str_mv 10.1016/j.cor.2005.09.019
format article
fullrecord <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_29990608</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><els_id>S0305054805003114</els_id><sourcerecordid>29990608</sourcerecordid><originalsourceid>FETCH-LOGICAL-c385t-d89bd925f60a74dba492e7ff158ede8aaaaa4ebf9a702c7d0f64a6a518a137a33</originalsourceid><addsrcrecordid>eNp9kMFq3DAQhkVpoNukD9CbCaQ3OyPbsiR6CiFtCoFcEtKbmJVGVBuvtZW8Cfv21bILgR4yl5nDN_8MH2NfOTQc-HC5amxMTQsgGtANcP2BLbiSXS0H8fsjW0AHogbRq0_sc84rKCVbvmBXT5jWVZ4xzdVyV93GjQ80umqibcKxtPk1pudc-ZiqMM2UQhk2sYzVmuY_0eUzduJxzPTl2E_Z44-bh-vb-u7-56_rq7vadkrMtVN66XQr_AAoe7fEXrckvedCkSOF--pp6TVKaK104IceBxRcIe8kdt0p-3bI3aT4d0t5NuuQLY0jThS32bRaaxhAFfD8P3AVt2kqvxmuhepBcF4gfoBsijkn8maTwhrTznAwe6NmZYpRszdqQJtitOxcHIMxWxx9wsmG_LaohCz5Q-G-HzgqOl4CJZNtoMmSC4nsbFwM71z5B6ODi9s</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>195840511</pqid></control><display><type>article</type><title>Warm start by Hopfield neural networks for interior point methods</title><source>ScienceDirect Freedom Collection</source><creator>Fontova, Marta I. Velazco ; Oliveira, Aurelio R.L. ; Lyra, Christiano</creator><creatorcontrib>Fontova, Marta I. Velazco ; Oliveira, Aurelio R.L. ; Lyra, Christiano</creatorcontrib><description>Hopfield neural networks and interior point methods are used in an integrated way to solve linear optimization problems. The Hopfield network gives warm start for the primal–dual interior point methods, which can be way ahead in the path to optimality. The approaches were applied to a set of real world linear programming problems. The integrated approaches provide promising results, indicating that there may be a place for neural networks in the “real game” of optimization.</description><identifier>ISSN: 0305-0548</identifier><identifier>EISSN: 1873-765X</identifier><identifier>EISSN: 0305-0548</identifier><identifier>DOI: 10.1016/j.cor.2005.09.019</identifier><identifier>CODEN: CMORAP</identifier><language>eng</language><publisher>Oxford: Elsevier Ltd</publisher><subject>Applied sciences ; Artificial intelligence ; Computer science; control theory; systems ; Connectionism. Neural networks ; Exact sciences and technology ; Hopfield networks ; Linear programming ; Neural networks ; Operational research. Management science ; Optimization techniques ; Primal–dual interior point methods ; Problem solving ; Studies</subject><ispartof>Computers &amp; operations research, 2007-09, Vol.34 (9), p.2553-2561</ispartof><rights>2005 Elsevier Ltd</rights><rights>2007 INIST-CNRS</rights><rights>Copyright Pergamon Press Inc. Sep 2007</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c385t-d89bd925f60a74dba492e7ff158ede8aaaaa4ebf9a702c7d0f64a6a518a137a33</citedby><cites>FETCH-LOGICAL-c385t-d89bd925f60a74dba492e7ff158ede8aaaaa4ebf9a702c7d0f64a6a518a137a33</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&amp;idt=18579586$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><creatorcontrib>Fontova, Marta I. Velazco</creatorcontrib><creatorcontrib>Oliveira, Aurelio R.L.</creatorcontrib><creatorcontrib>Lyra, Christiano</creatorcontrib><title>Warm start by Hopfield neural networks for interior point methods</title><title>Computers &amp; operations research</title><description>Hopfield neural networks and interior point methods are used in an integrated way to solve linear optimization problems. The Hopfield network gives warm start for the primal–dual interior point methods, which can be way ahead in the path to optimality. The approaches were applied to a set of real world linear programming problems. The integrated approaches provide promising results, indicating that there may be a place for neural networks in the “real game” of optimization.</description><subject>Applied sciences</subject><subject>Artificial intelligence</subject><subject>Computer science; control theory; systems</subject><subject>Connectionism. Neural networks</subject><subject>Exact sciences and technology</subject><subject>Hopfield networks</subject><subject>Linear programming</subject><subject>Neural networks</subject><subject>Operational research. Management science</subject><subject>Optimization techniques</subject><subject>Primal–dual interior point methods</subject><subject>Problem solving</subject><subject>Studies</subject><issn>0305-0548</issn><issn>1873-765X</issn><issn>0305-0548</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2007</creationdate><recordtype>article</recordtype><recordid>eNp9kMFq3DAQhkVpoNukD9CbCaQ3OyPbsiR6CiFtCoFcEtKbmJVGVBuvtZW8Cfv21bILgR4yl5nDN_8MH2NfOTQc-HC5amxMTQsgGtANcP2BLbiSXS0H8fsjW0AHogbRq0_sc84rKCVbvmBXT5jWVZ4xzdVyV93GjQ80umqibcKxtPk1pudc-ZiqMM2UQhk2sYzVmuY_0eUzduJxzPTl2E_Z44-bh-vb-u7-56_rq7vadkrMtVN66XQr_AAoe7fEXrckvedCkSOF--pp6TVKaK104IceBxRcIe8kdt0p-3bI3aT4d0t5NuuQLY0jThS32bRaaxhAFfD8P3AVt2kqvxmuhepBcF4gfoBsijkn8maTwhrTznAwe6NmZYpRszdqQJtitOxcHIMxWxx9wsmG_LaohCz5Q-G-HzgqOl4CJZNtoMmSC4nsbFwM71z5B6ODi9s</recordid><startdate>20070901</startdate><enddate>20070901</enddate><creator>Fontova, Marta I. Velazco</creator><creator>Oliveira, Aurelio R.L.</creator><creator>Lyra, Christiano</creator><general>Elsevier Ltd</general><general>Elsevier Science</general><general>Pergamon Press Inc</general><scope>IQODW</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope></search><sort><creationdate>20070901</creationdate><title>Warm start by Hopfield neural networks for interior point methods</title><author>Fontova, Marta I. Velazco ; Oliveira, Aurelio R.L. ; Lyra, Christiano</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c385t-d89bd925f60a74dba492e7ff158ede8aaaaa4ebf9a702c7d0f64a6a518a137a33</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2007</creationdate><topic>Applied sciences</topic><topic>Artificial intelligence</topic><topic>Computer science; control theory; systems</topic><topic>Connectionism. Neural networks</topic><topic>Exact sciences and technology</topic><topic>Hopfield networks</topic><topic>Linear programming</topic><topic>Neural networks</topic><topic>Operational research. Management science</topic><topic>Optimization techniques</topic><topic>Primal–dual interior point methods</topic><topic>Problem solving</topic><topic>Studies</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Fontova, Marta I. Velazco</creatorcontrib><creatorcontrib>Oliveira, Aurelio R.L.</creatorcontrib><creatorcontrib>Lyra, Christiano</creatorcontrib><collection>Pascal-Francis</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts – Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>Computers &amp; operations research</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Fontova, Marta I. Velazco</au><au>Oliveira, Aurelio R.L.</au><au>Lyra, Christiano</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Warm start by Hopfield neural networks for interior point methods</atitle><jtitle>Computers &amp; operations research</jtitle><date>2007-09-01</date><risdate>2007</risdate><volume>34</volume><issue>9</issue><spage>2553</spage><epage>2561</epage><pages>2553-2561</pages><issn>0305-0548</issn><eissn>1873-765X</eissn><eissn>0305-0548</eissn><coden>CMORAP</coden><abstract>Hopfield neural networks and interior point methods are used in an integrated way to solve linear optimization problems. The Hopfield network gives warm start for the primal–dual interior point methods, which can be way ahead in the path to optimality. The approaches were applied to a set of real world linear programming problems. The integrated approaches provide promising results, indicating that there may be a place for neural networks in the “real game” of optimization.</abstract><cop>Oxford</cop><pub>Elsevier Ltd</pub><doi>10.1016/j.cor.2005.09.019</doi><tpages>9</tpages></addata></record>
fulltext fulltext
identifier ISSN: 0305-0548
ispartof Computers & operations research, 2007-09, Vol.34 (9), p.2553-2561
issn 0305-0548
1873-765X
0305-0548
language eng
recordid cdi_proquest_miscellaneous_29990608
source ScienceDirect Freedom Collection
subjects Applied sciences
Artificial intelligence
Computer science
control theory
systems
Connectionism. Neural networks
Exact sciences and technology
Hopfield networks
Linear programming
Neural networks
Operational research. Management science
Optimization techniques
Primal–dual interior point methods
Problem solving
Studies
title Warm start by Hopfield neural networks for interior point methods
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-25T05%3A10%3A22IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Warm%20start%20by%20Hopfield%20neural%20networks%20for%20interior%20point%20methods&rft.jtitle=Computers%20&%20operations%20research&rft.au=Fontova,%20Marta%20I.%20Velazco&rft.date=2007-09-01&rft.volume=34&rft.issue=9&rft.spage=2553&rft.epage=2561&rft.pages=2553-2561&rft.issn=0305-0548&rft.eissn=1873-765X&rft.coden=CMORAP&rft_id=info:doi/10.1016/j.cor.2005.09.019&rft_dat=%3Cproquest_cross%3E29990608%3C/proquest_cross%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c385t-d89bd925f60a74dba492e7ff158ede8aaaaa4ebf9a702c7d0f64a6a518a137a33%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=195840511&rft_id=info:pmid/&rfr_iscdi=true