S.M. Sadjadi, S. Chen, et al.
TAPIA 2009
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.
S.M. Sadjadi, S. Chen, et al.
TAPIA 2009
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SPIE Photomask Technology + EUV Lithography 2007
S.F. Fan, W.B. Yun, et al.
Proceedings of SPIE 1989
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