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» A Guided Monte Carlo Approach to Optimization Problems
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SIAMJO
2002
124views more  SIAMJO 2002»
13 years 7 months ago
The Sample Average Approximation Method for Stochastic Discrete Optimization
In this paper we study a Monte Carlo simulation based approach to stochastic discrete optimization problems. The basic idea of such methods is that a random sample is generated and...
Anton J. Kleywegt, Alexander Shapiro, Tito Homem-d...
ISCOPE
1998
Springer
13 years 11 months ago
Parallel Object Oriented Monte Carlo Simulations
Abstract. We discuss the parallelization and object-oriented implementation of Monte Carlo simulations for physical problems. We present a C++ Monte Carlo class library for the aut...
Matthias Troyer, Beat Ammon, Elmar Heeb
WSC
2004
13 years 8 months ago
Approximating Free Exercise Boundaries for American-Style Options Using Simulation and Optimization
Monte Carlo simulation can be readily applied to asset pricing problems with multiple state variables and possible path dependencies because convergence of Monte Carlo methods is ...
Barry R. Cobb, John M. Charnes
GECCO
2009
Springer
156views Optimization» more  GECCO 2009»
14 years 1 months ago
Articulating user preferences in many-objective problems by sampling the weighted hypervolume
The hypervolume indicator has become popular in recent years both for performance assessment and to guide the search of evolutionary multiobjective optimizers. Two critical resear...
Anne Auger, Johannes Bader, Dimo Brockhoff, Eckart...
ACL
2011
12 years 11 months ago
Learning to Win by Reading Manuals in a Monte-Carlo Framework
This paper presents a novel approach for leveraging automatically extracted textual knowledge to improve the performance of control applications such as games. Our ultimate goal i...
S. R. K. Branavan, David Silver, Regina Barzilay