Gradient-based simulation optimization
Proceedings of the 38th conference on Winter simulation
The stochastic root-finding problem: Overview, solutions, and open questions
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Averaging and derivative estimation within stochastic approximation algorithms
Proceedings of the Winter Simulation Conference
An introspective on the retrospective-approximation paradigm
Proceedings of the Winter Simulation Conference
Root finding via darts: dynamic adaptive random target shooting
Proceedings of the Winter Simulation Conference
Structural and Multidisciplinary Optimization
Multidimensional stochastic approximation: Adaptive algorithms and applications
ACM Transactions on Modeling and Computer Simulation (TOMACS) - Special issue on simulation in complex service systems
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We extend the scaled-and-shifted Kiefer-Wolfowitz (SSKW) algorithm developed by Broadie, Cicek, and Zeevi (2009) to multiple dimensions. The salient feature of this algorithm is that it makes adjustments of the tuning parameters that adapt to the underlying problem characteristics. We compare the performance of this algorithm to the traditional Kiefer-Wolfowitz (KW) one and observe significant improvement in the finite-time performance on some stylized test functions and a multidimensional newsvendor problem.