Now showing items 1-7 of 7
Clustering of sample average approximation for stochastic program
We present an improvement to the Sample Average Approximation (SAA) method for two-stage stochasticprogram. Although the SAA has nice theoretical properties, such as convergence in probability and consistency,as long as ...
Convergence of the Smoothed Empirical Process in Nested Distance
The nested distance, also process distance, provides a quantitative measure of distance for stochastic processes. It is the crucial and determining distance for stochastic optimization problems.In this paper we demonstrate ...
Parallel stochastic optimization based on descent algorithms
This study addresses the stochastic optimization of a function unknown in closed form which can only be estimated based on measurementsor simulations. We consider parallel implementations of a class of stochasticoptimization ...
A Simulation Based Approach to Solve A Specific Type of Chance Constrained Optimization
We solve the chance constrained optimization with convexfeasible set through approximating the chance constraint by another convexsmooth function. The approximation is based on the numerical properties of theBernstein ...
Statistical Estimation of Composite Risk Functionals and Risk Optimization Problems
We address the statistical estimation of composite functionals whichmay be nonlinear in the probability measure. Our study is motivated bythe need to estimate coherent measures of risk, which become increasinglypopular in ...
A Comment on "Computational Complexityof Stochastic Programming Problems"
Although stochastic programming problems were always believed to be computationally chal-lenging, this perception has only recently received a theoretical justification by the seminal workof Dyer and Stougie (Mathematical ...