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

• Published in: Neurocomputing (Amsterdam) Vol. 72; no. 7; pp. 1679 - 1687
• Main Authors: Zhang, Yunong, Ma, Weimu, Li, Xiao-Dong, Tan, Hong-Zhou, Chen, Ke
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.03.2009
• Subjects: Circuit implementation; Linear programs; MATLAB Simulink modeling and simulation; Neural networks; Quadratic programs; Quadratic programs; Circuit implementation; Neural networks; Linear programs; MATLAB Simulink modeling and simulation
• ISSN: 0925-2312; 1872-8286
• DOI: 10.1016/j.neucom.2008.07.008

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.