Journal Article10.1002/NAV.3800190407
A mathematical programming approach to identification and optimization of a class of unknown systems
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TL;DR: Three approaches to the identification-optimization problem are proposed: an outer-linearized approximation using relaxation (OLR); an inner-linearization approximation using restriction (ILR); and a sequential combination of inner- and outer- linearized subproblems (SIO).
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Abstract: There exists a class of decision problems for which: (1) models of input-output response functions are not available in a closed-form, functional representation; (2) informational costs associated with learning about the response function are significant. For these problems, combining identification with optimization using mathematical programming is potentially attractive. Three approaches to the identification-optimization problem are proposed: an outer-linearized approximation using relaxation (OLR); an inner-linearized approximation using restriction (ILR); and a sequential combination of inner- and outer-linearized subproblems (SIO). Algorithms based on each approach are developed and computational experience reported.
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Citations
An Optimization Algorithm for a Linear Model of a Simulation System
TL;DR: This paper explores the normative theory of simulation within the context of an optimization algorithm for a linear programming model of the experimental setting and presents a sequential experimental design aimed at identifying the feasible levels of the policy variables which provide the optimal level of expected response.
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A simulation–optimization method: Its convergence and utility
Guy L. Curry,Darald J. Hartfiel +1 more
TL;DR: The method is superior to most other numerical optimization procedures, however, the class of problems for which the method is applicable is restricted to problems with enough known structure to generate a convergent iterative procedure.
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Information Improvement Bounds
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George B. Dantzig,Philip Wolfe +1 more
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Newton's method for convex programming and Tchebycheff approximation
E. W. Cheney,A. A. Goldstein +1 more
TL;DR: The rationale of Newton's method is exploited here in order to develop effective algorithms for solving the following general problem: given a convex continuous function F defined on a closed convex subset K of E,~, obtain a point x of K such that F(x)_<_F(y) for all y in K.
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