Sliding Modes in Solving Convex Programming Problems

Sliding modes are used to analyze a class of dynamical systems that solve convex programming problems. The analysis is carried out using concepts from the theory of differential equations with discontinuous right-hand sides and Lyapunov stability theory. It is shown that the equilibrium points of the system coincide with the minimizers of the convex programming problem, and that irrespective of the initial state of the system the state trajectory converges to the solution set of the problem. The dynamic behavior of the systems is illustrated by two numerical examples.

[1]  Insley B. Pyne,et al.  Linear programming on an electronic analogue computer , 1956, Transactions of the American Institute of Electrical Engineers, Part I: Communication and Electronics.

[2]  J. B. Rosen The gradient projection method for nonlinear programming: Part II , 1961 .

[3]  S. K. Korovin,et al.  Using sliding modes in static optimization and nonlinear programming , 1974, Autom..

[4]  Dimitri P. Bertsekas,et al.  Necessary and sufficient conditions for a penalty method to be exact , 1975, Math. Program..

[5]  Vadim I. Utkin,et al.  Sliding Modes and their Application in Variable Structure Systems , 1978 .

[6]  J. Aubin,et al.  Differential inclusions set-valued maps and viability theory , 1984 .

[7]  Yurij G. Evtushenko,et al.  Numerical Optimization Techniques , 1985 .

[8]  John J. Hopfield,et al.  Simple 'neural' optimization networks: An A/D converter, signal decision circuit, and a linear programming circuit , 1986 .

[9]  Leon O. Chua,et al.  Neural networks for nonlinear programming , 1988 .

[10]  Aleksej F. Filippov,et al.  Differential Equations with Discontinuous Righthand Sides , 1988, Mathematics and Its Applications.

[11]  Edgar Sanchez-Sinencio,et al.  Nonlinear switched capacitor 'neural' networks for optimization problems , 1990 .

[12]  Clóvis C. Gonzaga,et al.  Path-Following Methods for Linear Programming , 1992, SIAM Rev..

[13]  Sjur Didrik Flåm,et al.  Solving Convex Programs by Means of Ordinary Differential Equations , 1992, Math. Oper. Res..

[14]  L. Faybusovich Dynamical systems that solve linear programming problems , 1992, [1992] Proceedings of the 31st IEEE Conference on Decision and Control.

[15]  Vadim I. Utkin,et al.  Sliding Modes in Control and Optimization , 1992, Communications and Control Engineering Series.

[16]  Andrzej Cichocki,et al.  Neural networks for optimization and signal processing , 1993 .

[17]  Stefen Hui,et al.  On solving constrained optimization problems with neural networks: a penalty method approach , 1993, IEEE Trans. Neural Networks.

[18]  M. Vidyasagar Minimum-seeking properties of analog neural networks with multilinear objective functions , 1993, Proceedings of 32nd IEEE Conference on Decision and Control.

[19]  Stefen Hui,et al.  Neural networks for constrained optimization problems , 1993, Int. J. Circuit Theory Appl..

[20]  U. Helmke,et al.  Optimization and Dynamical Systems , 1994, Proceedings of the IEEE.

[21]  G. Lin Nonlinear Programming without Computation , 2022 .