Sigmoidic linear and non-linear optimization algorithms

J. C. Culioli; V. Protopopescu

doi:10.1016/0893-6080(88)90125-6

Sigmoidic linear and non-linear optimization algorithms

J. C. Culioli
, V. Protopopescu

Computational Sciences & Engineering Div

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

Abstract

Since Hopfield and Tank have proposed their neural network based computation method to solve optimization combinatorial problems, a significant amount of work has been done to apply it to a great number of difficult problems, with various degrees of success. Here, we address continuous optimization and propose neural network models for linear and non-linear optimization. We have tested both algorithms on several test-cases and found an excellent numerical behavior.

Original language	English
Pages (from-to)	86
Number of pages	1
Journal	Neural Networks
Volume	1
Issue number	1 SUPPL
DOIs	https://doi.org/10.1016/0893-6080(88)90125-6
State	Published - 1988
Event	International Neural Network Society 1988 First Annual Meeting - Boston, MA, USA Duration: Sep 6 1988 → Sep 10 1988

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title = "Sigmoidic linear and non-linear optimization algorithms",

abstract = "Since Hopfield and Tank have proposed their neural network based computation method to solve optimization combinatorial problems, a significant amount of work has been done to apply it to a great number of difficult problems, with various degrees of success. Here, we address continuous optimization and propose neural network models for linear and non-linear optimization. We have tested both algorithms on several test-cases and found an excellent numerical behavior.",

author = "Culioli, \{J. C.\} and V. Protopopescu",

year = "1988",

doi = "10.1016/0893-6080(88)90125-6",

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T1 - Sigmoidic linear and non-linear optimization algorithms

AU - Culioli, J. C.

AU - Protopopescu, V.

PY - 1988

Y1 - 1988

N2 - Since Hopfield and Tank have proposed their neural network based computation method to solve optimization combinatorial problems, a significant amount of work has been done to apply it to a great number of difficult problems, with various degrees of success. Here, we address continuous optimization and propose neural network models for linear and non-linear optimization. We have tested both algorithms on several test-cases and found an excellent numerical behavior.

AB - Since Hopfield and Tank have proposed their neural network based computation method to solve optimization combinatorial problems, a significant amount of work has been done to apply it to a great number of difficult problems, with various degrees of success. Here, we address continuous optimization and propose neural network models for linear and non-linear optimization. We have tested both algorithms on several test-cases and found an excellent numerical behavior.

UR - https://www.scopus.com/pages/publications/0024177510

U2 - 10.1016/0893-6080(88)90125-6

DO - 10.1016/0893-6080(88)90125-6

M3 - Conference article

AN - SCOPUS:0024177510

SN - 0893-6080

VL - 1

SP - 86

JO - Neural Networks

JF - Neural Networks

IS - 1 SUPPL

T2 - International Neural Network Society 1988 First Annual Meeting

Y2 - 6 September 1988 through 10 September 1988

ER -

Sigmoidic linear and non-linear optimization algorithms

Abstract

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