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WSC
2001
13 years 6 months ago
Monte Carlo simulation approach to stochastic programming
Various stochastic programmingproblemscan be formulated as problems of optimization of an expected value function. Quite often the corresponding expectation function cannot be com...
Alexander Shapiro
FOCS
2005
IEEE
13 years 10 months ago
Sampling-based Approximation Algorithms for Multi-stage Stochastic
Stochastic optimization problems provide a means to model uncertainty in the input data where the uncertainty is modeled by a probability distribution over the possible realizatio...
Chaitanya Swamy, David B. Shmoys
DAGSTUHL
2007
13 years 6 months ago
Sampling-based Approximation Algorithms for Multi-stage Stochastic Optimization
Stochastic optimization problems provide a means to model uncertainty in the input data where the uncertainty is modeled by a probability distribution over the possible realizatio...
Chaitanya Swamy, David B. Shmoys
CDC
2010
IEEE
104views Control Systems» more  CDC 2010»
13 years 6 days ago
Single timescale regularized stochastic approximation schemes for monotone Nash games under uncertainty
Abstract-- In this paper, we consider the distributed computation of equilibria arising in monotone stochastic Nash games over continuous strategy sets. Such games arise in setting...
Jayash Koshal, Angelia Nedic, Uday V. Shanbhag
SIAMCO
2000
117views more  SIAMCO 2000»
13 years 5 months ago
The O.D.E. Method for Convergence of Stochastic Approximation and Reinforcement Learning
It is shown here that stability of the stochastic approximation algorithm is implied by the asymptotic stability of the origin for an associated ODE. This in turn implies convergen...
Vivek S. Borkar, Sean P. Meyn