Charles H. Bennett, Aram W. Harrow, et al.
IEEE Trans. Inf. Theory
We consider simulation-optimization (SO) models where the decision variables are integer ordered and the objective function is defined implicitly via a simulation oracle, which for any feasible solution can be called to compute a point estimate of the objective-function value. We develop R-SPLINE-a Retrospective-search algorithm that alternates between a continuous Search using Piecewise-Linear Interpolation and a discrete Neighborhood Enumeration, to asymptotically identify a local minimum. R-SPLINE appears to be among the first few gradient-based search algorithms tailored for solving integer-ordered local SO problems. In addition to proving the almost-sure convergence of R-SPLINE's iterates to the set of local minima, we demonstrate that the probability of R-SPLINE returning a solution outside the set of true local minima decays exponentially in a certain precise sense. R-SPLINE, with no parameter tuning, compares favorably with popular existing algorithms. © 2013 ACM.
Charles H. Bennett, Aram W. Harrow, et al.
IEEE Trans. Inf. Theory
Raghu Krishnapuram, Krishna Kummamuru
IFSA 2003
Thomas M. Cover
IEEE Trans. Inf. Theory
J.P. Locquet, J. Perret, et al.
SPIE Optical Science, Engineering, and Instrumentation 1998