A Novel Separable Model and Decomposition Method for Sensor Locational Decision Problem

Abstract

This paper proposes a new separable model for the sensor locational decision problem covering a line (SLDPCL). By decomposing a multivariate function into several univariate functions, a separable outer approximation methodology that can be used to improve the outer approximation of classical convex programming technique is presented. A novel outer approximation method (OAM) for this proposed separable model is proposed. The algorithm alternates between solving a mixed integer linear program and a convex nonlinear program (NLP). An improved interior point method based on optimal centering parameter is employed to solve the NLP subproblem. The simulation results for test instances that range in size from 10 to 20000 sensors show that the proposed method is fast and robust, and the method is very promising for large-scale SLDPCL problems due to its excellent scalability.