Augmented Hopfield network for mixed-integer programming
Watta and Hassoun (1996) proposed a coupled gradient neural network for mixed integer programming. In this network continuous neurons were used to represent discrete variables. For the larger temporal problem they attempted many of the solutions found were infeasible. This paper proposes an augmente...
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Veröffentlicht in: | IEEE transactions on neural networks 1999-03, Vol.10 (2), p.456-458 |
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creator | Walsh, M.P. Flynn, M.E. O'Malley, M.J. |
description | Watta and Hassoun (1996) proposed a coupled gradient neural network for mixed integer programming. In this network continuous neurons were used to represent discrete variables. For the larger temporal problem they attempted many of the solutions found were infeasible. This paper proposes an augmented Hopfield network which is similar to the coupled gradient network proposed by Watta and Hassoun. However, in this network truly discrete neurons are used. It is shown that this network can be applied to mixed integer programming. Results illustrate that feasible solutions are now obtained for the larger temporal problem. |
doi_str_mv | 10.1109/72.750578 |
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Results illustrate that feasible solutions are now obtained for the larger temporal problem.</description><subject>Applied sciences</subject><subject>Electric, optical and optoelectronic circuits</subject><subject>Electronics</subject><subject>Exact sciences and technology</subject><subject>Linear programming</subject><subject>Mixed integer</subject><subject>Networks</subject><subject>Neural networks</subject><subject>Neurons</subject><subject>Programming</subject><subject>Temporal logic</subject><subject>Transfer functions</subject><issn>1045-9227</issn><issn>1941-0093</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>1999</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNqF0DtPwzAUBWALgWgpDKwMKAMCMaT4Ecf2WFVAkSqxwBw59k0UyKPYjYB_jyER3WDylfzp3KuD0CnBc0KwuhF0LjjmQu6hKVEJiTFWbD_MOOGxolRM0JH3LxiThOP0EE2IpJzyhE-RXPRlA-0WbLTqNkUFtY1a2L537jUqOhc11QfYuAqgBBdtXFc63TRVWx6jg0LXHk7Gd4ae726flqt4_Xj_sFysY8MU28YpWEGNLaRNsWWYU2qVEUbmRBprKWAtrDScAwdGdF5gCkQqbrhRKlEmZzN0NeSG3W89-G3WVN5AXesWut5niigVSpDsXykYowwnhAd5-aekIS0NfQZ4PUDjOu8dFNnGVY12nxnB2Xf1maDZUH2w52Nonzdgd3LsOoCLEWhvdF043ZrK75xgKfk57mxgFQD8_o5LvgBOoJIi</recordid><startdate>19990301</startdate><enddate>19990301</enddate><creator>Walsh, M.P.</creator><creator>Flynn, M.E.</creator><creator>O'Malley, M.J.</creator><general>IEEE</general><general>Institute of Electrical and Electronics Engineers</general><scope>RIA</scope><scope>RIE</scope><scope>IQODW</scope><scope>NPM</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><scope>7X8</scope><scope>7SP</scope><scope>F28</scope><scope>FR3</scope></search><sort><creationdate>19990301</creationdate><title>Augmented Hopfield network for mixed-integer programming</title><author>Walsh, M.P. ; Flynn, M.E. ; O'Malley, M.J.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c393t-6ed72cdf8d60d30522d9c7c8b18cdd2e0a7d8c55e5e31abf02e1895c5c9949cb3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>1999</creationdate><topic>Applied sciences</topic><topic>Electric, optical and optoelectronic circuits</topic><topic>Electronics</topic><topic>Exact sciences and technology</topic><topic>Linear programming</topic><topic>Mixed integer</topic><topic>Networks</topic><topic>Neural networks</topic><topic>Neurons</topic><topic>Programming</topic><topic>Temporal logic</topic><topic>Transfer functions</topic><toplevel>online_resources</toplevel><creatorcontrib>Walsh, M.P.</creatorcontrib><creatorcontrib>Flynn, M.E.</creatorcontrib><creatorcontrib>O'Malley, M.J.</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE</collection><collection>Pascal-Francis</collection><collection>PubMed</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><collection>MEDLINE - Academic</collection><collection>Electronics & Communications Abstracts</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><jtitle>IEEE transactions on neural networks</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Walsh, M.P.</au><au>Flynn, M.E.</au><au>O'Malley, M.J.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Augmented Hopfield network for mixed-integer programming</atitle><jtitle>IEEE transactions on neural networks</jtitle><stitle>TNN</stitle><addtitle>IEEE Trans Neural Netw</addtitle><date>1999-03-01</date><risdate>1999</risdate><volume>10</volume><issue>2</issue><spage>456</spage><epage>458</epage><pages>456-458</pages><issn>1045-9227</issn><eissn>1941-0093</eissn><coden>ITNNEP</coden><abstract>Watta and Hassoun (1996) proposed a coupled gradient neural network for mixed integer programming. In this network continuous neurons were used to represent discrete variables. For the larger temporal problem they attempted many of the solutions found were infeasible. This paper proposes an augmented Hopfield network which is similar to the coupled gradient network proposed by Watta and Hassoun. However, in this network truly discrete neurons are used. It is shown that this network can be applied to mixed integer programming. Results illustrate that feasible solutions are now obtained for the larger temporal problem.</abstract><cop>New York, NY</cop><pub>IEEE</pub><pmid>18252545</pmid><doi>10.1109/72.750578</doi><tpages>3</tpages></addata></record> |
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subjects | Applied sciences Electric, optical and optoelectronic circuits Electronics Exact sciences and technology Linear programming Mixed integer Networks Neural networks Neurons Programming Temporal logic Transfer functions |
title | Augmented Hopfield network for mixed-integer programming |
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