Matrix analysis
Operations Research
Some guidelines and guarantees for common random numbers
Management Science
Correlation induction without the inverse transformation
WSC '86 Proceedings of the 18th conference on Winter simulation
Discrete-event simulation
Convex Optimization
An Introduction to Copulas (Springer Series in Statistics)
An Introduction to Copulas (Springer Series in Statistics)
Simulation Modeling and Analysis (McGraw-Hill Series in Industrial Engineering and Management)
Simulation Modeling and Analysis (McGraw-Hill Series in Industrial Engineering and Management)
Comparing two systems: beyond common random numbers
Proceedings of the 40th Conference on Winter Simulation
Adaptive Control Variates for Finite-Horizon Simulation
Mathematics of Operations Research
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A common problem in operations research involves comparing two system designs through simulation of both systems. The comparison can often be made more accurate through careful control (coupling) of the random numbers that are used in simulating each system, with common random numbers being the standard example. We describe a new approach for coupling the random-number inputs to two systems that involves generating realizations of a Gaussian random vector and then transforming the Gaussian random vector into the desired random-number inputs. We use nonlinear semidefinite programming to select the correlation matrix of the Gaussian random vector, with the goal of sharpening the comparison.