Converting general nonlinear programming problems into separable programming problems with feedforward neural networks.

DOI10.1016/S0893-6080(02)00234-4zbMath1255.90112OpenAlexW2096252678WikidataQ51728551 ScholiaQ51728551MaRDI QIDQ1422260

Koji Ito, Bao-Liang Lu

Publication date: 9 February 2004

Published in: Neural Networks (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/s0893-6080(02)00234-4

zbMATH Keywords

nonlinear programmingseparable programmingsimplex methodfeedforward neural networkpiecewise linear approximationseparable function

Mathematics Subject Classification ID

Nonlinear programming (90C30) Linear programming (90C05) Approximation methods and heuristics in mathematical programming (90C59)

Cites Work

• Multilayer feedforward networks are universal approximators
• An approach to nonlinear programming
• Learning representations by back-propagating errors
• The Variable Reduction Method for Nonlinear Programming
• Approximation by superpositions of a sigmoidal function
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