@article{2013wanpassch,
	author      =   {H. Wang and R. Pasupathy and B. W. Schmeiser},
	title       =   {Integer-Ordered Simulation Optimization using {R-SPLINE}: Retrospective Search using Piecewise-Linear 	Interpolation and Neighborhood Enumeration},
	journal     =   {ACM TOMACS},
	year        =   {2013},
	volume      =   {23},
	number      =   {3},
	pages       =   {},
	doi = {10.1145/2499913.2499916},
	url = {http://web.ics.purdue.edu/~pasupath/PAPERS/2013wanpassch.pdf},
	keywords = {retrospective approximation, simulation optimization on integer-ordered spaces},
	abstract = {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.}}

{"_id":"PJ5AzgAXbNoxLnZpA","bibbaseid":"wang-pasupathy-schmeiser-integerorderedsimulationoptimizationusingrsplineretrospectivesearchusingpiecewiselinearinterpolationandneighborhoodenumeration-2013","author_short":["Wang, H.","Pasupathy, R.","Schmeiser, B. W."],"bibdata":{"bibtype":"article","type":"article","author":[{"firstnames":["H."],"propositions":[],"lastnames":["Wang"],"suffixes":[]},{"firstnames":["R."],"propositions":[],"lastnames":["Pasupathy"],"suffixes":[]},{"firstnames":["B.","W."],"propositions":[],"lastnames":["Schmeiser"],"suffixes":[]}],"title":"Integer-Ordered Simulation Optimization using R-SPLINE: Retrospective Search using Piecewise-Linear Interpolation and Neighborhood Enumeration","journal":"ACM TOMACS","year":"2013","volume":"23","number":"3","pages":"","doi":"10.1145/2499913.2499916","url":"http://web.ics.purdue.edu/~pasupath/PAPERS/2013wanpassch.pdf","keywords":"retrospective approximation, simulation optimization on integer-ordered spaces","abstract":"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.","bibtex":"@article{2013wanpassch,\n\tauthor = {H. Wang and R. Pasupathy and B. W. Schmeiser},\n\ttitle = {Integer-Ordered Simulation Optimization using {R-SPLINE}: Retrospective Search using Piecewise-Linear \tInterpolation and Neighborhood Enumeration},\n\tjournal = {ACM TOMACS},\n\tyear = {2013},\n\tvolume = {23},\n\tnumber = {3},\n\tpages = {},\n\tdoi = {10.1145/2499913.2499916},\n\turl = {http://web.ics.purdue.edu/~pasupath/PAPERS/2013wanpassch.pdf},\n\tkeywords = {retrospective approximation, simulation optimization on integer-ordered spaces},\n\tabstract = {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.}}\n\n","author_short":["Wang, H.","Pasupathy, R.","Schmeiser, B. W."],"key":"2013wanpassch","id":"2013wanpassch","bibbaseid":"wang-pasupathy-schmeiser-integerorderedsimulationoptimizationusingrsplineretrospectivesearchusingpiecewiselinearinterpolationandneighborhoodenumeration-2013","role":"author","urls":{"Paper":"http://web.ics.purdue.edu/~pasupath/PAPERS/2013wanpassch.pdf"},"keyword":["retrospective approximation","simulation optimization on integer-ordered spaces"],"metadata":{"authorlinks":{}},"downloads":0,"html":""},"bibtype":"article","biburl":"web.ics.purdue.edu/~pasupath/rpVitapublist.bib","dataSources":["Q4TNB6PfH2Ne5F2fj"],"keywords":["retrospective approximation","simulation optimization on integer-ordered spaces"],"search_terms":["integer","ordered","simulation","optimization","using","spline","retrospective","search","using","piecewise","linear","interpolation","neighborhood","enumeration","wang","pasupathy","schmeiser"],"title":"Integer-Ordered Simulation Optimization using R-SPLINE: Retrospective Search using Piecewise-Linear Interpolation and Neighborhood Enumeration","year":2013}