A Recurrent Neural Network for Solving Strictly Convex Quadratic Programming Problems

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GHOMASHI A.; ABBASI M.

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Journal Paper

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Journal: International Journal of Industrial Mathematics Year:2018 | Volume:10 | Issue:4 Page(s): 339-347

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Title

A Recurrent Neural Network for Solving Strictly Convex Quadratic Programming Problems

Author(s)

GHOMASHI A. | ABBASI M. | Issue Writer Certificate

Keywords

Dynamical system

Strictly convex quadratic programming

Stability

Global convergence

Recurrent neural networks

Abstract

In this paper, we present an improved neural network to solve strictly convex quadratic program-ming(QP) problem. The proposed model includes a set of di erential equations such that their equi-librium points correspond to optimality condition of convex (QP) problem and has a lower structure complexity respect to the other existing neural network model for solving such problems. In theoret-ical aspect, Stability and Global convergence of the proposed neural network is proved. The validity and transient behavior of the proposed neural network are demonstrated by using four numerical examples.

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APA: Copy

GHOMASHI, A., & ABBASI, M.. (2018). A Recurrent Neural Network for Solving Strictly Convex Quadratic Programming Problems. INTERNATIONAL JOURNAL OF INDUSTRIAL MATHEMATICS, 10(4), 339-347. SID. https://sid.ir/paper/739960/en

Vancouver: Copy

GHOMASHI A., ABBASI M.. A Recurrent Neural Network for Solving Strictly Convex Quadratic Programming Problems. INTERNATIONAL JOURNAL OF INDUSTRIAL MATHEMATICS[Internet]. 2018;10(4):339-347. Available from: https://sid.ir/paper/739960/en

IEEE: Copy

A. GHOMASHI, and M. ABBASI, “A Recurrent Neural Network for Solving Strictly Convex Quadratic Programming Problems,” INTERNATIONAL JOURNAL OF INDUSTRIAL MATHEMATICS, vol. 10, no. 4, pp. 339–347, 2018, [Online]. Available: https://sid.ir/paper/739960/en

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