Time-varying linear programming via LVI-PDNN with numerical examples

Playing a fundamental role in mathematic optimization, linear programming (LP) problems have been widely encountered in various scientific disciplines and industrial applications. Although static LP problems have been investigated extensively and applied to abundant scientific fields through the las...

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Bibliographic Details
Published inInternational Conference on Signal Processing (Print) pp. 326 - 331
Main Authors Sheng Wu, Liu He, Maotai Zou, Jian Li, Yunong Zhang
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.11.2016
Subjects
Computer simulation
Linear programming
linear variational inequality
Mathematical model
MATLAB
Neural networks
primal-dual neural network
Real-time systems
Time-varying linear programming
Time-varying systems
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ISBN9781509013449
150901344X
ISSN2164-5221
DOI10.1109/ICSP.2016.7877849

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Summary:Playing a fundamental role in mathematic optimization, linear programming (LP) problems have been widely encountered in various scientific disciplines and industrial applications. Although static LP problems have been investigated extensively and applied to abundant scientific fields through the last decades, researches concerning time-varying linear programming (TVLP) problem solving are in relatively small amount. In this paper, the TVLP problems are solved by a linear-variational-inequality based primal-dual neural network (LVI-PDNN), which is originally designed for static LP problem solving. Numerical examples and computer simulations further reveal that LVI-PDNN could approach the theoretical solution when solving TVLP problems subject to equality, inequality and bound constraints simultaneously.
ISBN:9781509013449
150901344X
ISSN:2164-5221
DOI:10.1109/ICSP.2016.7877849