Dynamical neural schema for quadratic discrete optimizations problems

E. Goles; G. Hernandez; M. Matamala

doi:10.1016/0893-6080(88)90135-9

Dynamical neural schema for quadratic discrete optimizations problems

E. Goles, G. Hernandez, M. Matamala

Research output: Contribution to journal › Conference article › peer-review

2 Scopus citations

Abstract

We present a Dynamical Neural Schema (DNS) that gives very good approximation at global minimum of discrete quadratic problem (P). We test DNS with classical schema and for fixed sigmoid functions. For the computational experiments we take 20×20 symmetric matrices, where we known the global minimum on {0,1}ⁿ, and also 40×40 random symmetric matrices. In almost all the cases DNS is better. Experimental comparisons will be given and also theoretical results concerning relations between fixed points of DNS and global optimum at problem (P).

Original language	English
Pages (from-to)	96
Number of pages	1
Journal	Neural Networks
Volume	1
Issue number	1 SUPPL
DOIs	https://doi.org/10.1016/0893-6080(88)90135-9
State	Published - 1988
Externally published	Yes
Event	International Neural Network Society 1988 First Annual Meeting - Boston, MA, USA Duration: 6 Sep 1988 → 10 Sep 1988

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title = "Dynamical neural schema for quadratic discrete optimizations problems",

abstract = "We present a Dynamical Neural Schema (DNS) that gives very good approximation at global minimum of discrete quadratic problem (P). We test DNS with classical schema and for fixed sigmoid functions. For the computational experiments we take 20×20 symmetric matrices, where we known the global minimum on {0,1}n, and also 40×40 random symmetric matrices. In almost all the cases DNS is better. Experimental comparisons will be given and also theoretical results concerning relations between fixed points of DNS and global optimum at problem (P).",

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AB - We present a Dynamical Neural Schema (DNS) that gives very good approximation at global minimum of discrete quadratic problem (P). We test DNS with classical schema and for fixed sigmoid functions. For the computational experiments we take 20×20 symmetric matrices, where we known the global minimum on {0,1}n, and also 40×40 random symmetric matrices. In almost all the cases DNS is better. Experimental comparisons will be given and also theoretical results concerning relations between fixed points of DNS and global optimum at problem (P).

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DO - 10.1016/0893-6080(88)90135-9

M3 - Conference article

AN - SCOPUS:0024168032

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JF - Neural Networks

IS - 1 SUPPL

T2 - International Neural Network Society 1988 First Annual Meeting

Y2 - 6 September 1988 through 10 September 1988

ER -

Dynamical neural schema for quadratic discrete optimizations problems

Abstract

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