A number of heuristic algorithms exist for determining whether each of r systems, characterized by m performance measures estimated through Monte Carlo simulation, belongs to a given set, which is defined by a finite collection of linear inequalities. The work here provides a heuristic for addressing a version of this problem in which the feasible region is defined by a finite collection of nonlinear inequalities. This approach allows the user to choose a desired level of confidence with which a system is correctly classified. The algorithm then uses appropriate-sized confidence rectangles centered at the estimated means to decide when a system can be classified with that level of confidence. While the worst-case behavior could potentially be bad, computational experiments show that the performance of the algorithm on randomly generated problems is satisfactory.
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Feasibility Determination When the Feasible Region is Defined by Non-Linear Inequalities
Semantic Scholar · Computer Science · 2020
Abstract
A number of heuristic algorithms exist for determining whether each of r systems, characterized by m performance measures estimated through Monte Carlo simulation, belongs to a given set, which is defined by a finite collection of linear inequalities. The work here provides a heuristic for addressing a version of this problem in which the feasible region is defined by a finite collection of nonlinear inequalities. This approach allows the user to choose a desired level of confidence with which a system is correctly classified. The algorithm then uses appropriate-sized confidence rectangles centered at the estimated means to decide when a system can be classified with that level of confidence. While the worst-case behavior could potentially be bad, computational experiments show that the performance of the algorithm on randomly generated problems is satisfactory.