MATLAB Simulink modeling and simulation of LVI-based primal-dual neural network for solving linear and quadratic programs

Authors:
Yunong Zhang;Weimu Ma;Xiao-Dong Li;Hong-Zhou Tan;Ke Chen
Affiliations:
School of Information Science and Technology, Sun Yat-Sen University, Guangzhou 510275, China;School of Information Science and Technology, Sun Yat-Sen University, Guangzhou 510275, China;School of Information Science and Technology, Sun Yat-Sen University, Guangzhou 510275, China;School of Information Science and Technology, Sun Yat-Sen University, Guangzhou 510275, China;School of Software, Sun Yat-Sen University, Guangzhou 510275, China
Venue:
Neurocomputing
Year:
2009

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Abstract

In view of parallel-processing nature and circuit-implementation convenience, recurrent neural networks are often employed to solve optimization problems. Recently, a primal-dual neural network based on linear variational inequalities (LVI) was developed by Zhang et al. for the online solution of linear-programming (LP) and quadratic-programming (QP) problems simultaneously subject to equality, inequality and bound constraints. For the final purpose of field programmable gate array (FPGA) and application-specific integrated circuit (ASIC) realization, we investigate in this paper the MATLAB Simulink modeling and simulative verification of such an LVI-based primal-dual neural network (LVI-PDNN). By using click-and-drag mouse operations in MATLAB Simulink environment, we could quickly model and simulate complicated dynamic systems. Modeling and simulative results substantiate the theoretical analysis and efficacy of the LVI-PDNN for solving online the linear and quadratic programs.