The algorithm uses sampling when the random variables are represented by continuous distribution functions. Traditionally, this method has been applied by using ...

Improving convergence of the stochastic decomposition algorithm by using an efficient sampling technique. Jose M. Ponce-Ortegaa, Vicente Rico-Ramireza ...

This work proposes to replace the use of the Monte Carlo Sampling Technique in the SD algorithm by the use of the Hammersley Sequence Sampling (HSS) technique.

Recently, such a technique has proved to provide better uniformity properties than other sampling techniques and, as a consequence, the variance and the number ...

Jan 1, 2003 · Traditionally, this method has been applied by using the Monte Carlo sampling technique to generate the samples of the stochastic variables.

This paper surveys the use of Monte Carlo sampling-based methods for stochastic optimization problems. Such methods are required when—as it often happens in ...

Abstract. In this paper, we present a sequential sampling-based algorithm for the two-stage. 4 distributionally robust linear program (2-DRLP) with general ...

dimension of the problem and y axis the convergence criteria using various sampling technique. ... Dige, Nishant & Diwekar, Urmila “Effective sampling techniques ...

Missing: efficient | Show results with:efficient

Importance sampling is a promising strategy for improving the convergence rate of stochastic gra- dient methods. It is typically used to precondi- tion the ...