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Variance Reduction for Sequential Sampling in Stochastic Programming [article]

Jangho Park and Rebecca Stockbridge and Güzin Bayraksan
2020 arXiv   pre-print
This paper investigates the variance reduction techniques Antithetic Variates (AV) and Latin Hypercube Sampling (LHS) when used for sequential sampling in stochastic programming and presents a comparative  ...  , AV and LHS sequential procedures present attractive alternatives in practice for a class of stochastic programs.  ...  Related Literature Numerous variance reduction techniques have been studied for stochastic programming.  ... 
arXiv:2005.02458v2 fatcat:sv6g3oht5zhwdgmxvqrwnv34b4

A combined deterministic and sampling-based sequential bounding method for stochastic programming

Peguy Pierre-Louis, Guzin Bayraksan, David P. Morton
2011 Proceedings of the 2011 Winter Simulation Conference (WSC)  
We develop an algorithm for two-stage stochastic programming with a convex second stage program and with uncertainty in the right-hand side.  ...  An upper bound estimator is formed through a stratified Monte Carlo sampling procedure that includes the use of a control variate variance reduction scheme.  ...  Variance Reduction We study variance reduction in SSAM for the different estimators we have proposed.  ... 
doi:10.1109/wsc.2011.6148105 dblp:conf/wsc/Pierre-LouisBM11 fatcat:meqmufl3qvgp5cmhbcdmdptizi

Assessing Solution Quality in Stochastic Programs via Sampling [chapter]

Güzin Bayraksan, David P. Morton
2009 Decision Technologies and Applications  
This scheme can be used as a stand-alone sequential sampling procedure, or it can be used in conjunction with a variety of sampling-based algorithms to obtain a solution to a stochastic program with a  ...  An alternative approach in stochastic programming is to use Monte Carlo sampling-based estimators on the optimality gap.  ...  The authors thank Georg Pflug for valuable discussions with respect to Example 5 and are grateful to Jeff Linderoth for his helpful comments that improved an earlier draft.  ... 
doi:10.1287/educ.1090.0065 fatcat:6tofe76lpje4bol2n6lrkjfxnu

Reliability evaluation of composite power systems using sequential simulation with Latin Hypercube Sampling

Zhen Shu, Panida Jirutitijaroen, Bordin Bordeerath
2014 2014 Power Systems Computation Conference  
In this paper, a new sequential simulation approach is proposed for reliability evaluation of composite power systems.  ...  The main idea is to apply Latin Hypercube sampling (LHS) to generate the time duration of each system state in order to facilitate simulation convergence.  ...  In [9] , LHS was adopted as a variance reduction tool for generating capacity reliability evaluation via non-sequential simulation.  ... 
doi:10.1109/pscc.2014.7038440 dblp:conf/pscc/ShuJB14 fatcat:n4eftceeqremlexiloayovu6hq

A Sequential Sampling Procedure for Stochastic Programming

Güzin Bayraksan, David P. Morton
2011 Operations Research  
We develop a sequential sampling procedure for a class of stochastic programs. We assume that a sequence of feasible solutions with an optimal limit point is given as input to our procedure.  ...  Such a sequence can be generated by solving a series of sampling problems with increasing sample size, or it can be found by any other viable method.  ...  An earlier abbreviated version of this paper appeared in Bayraksan and Morton (2007) .  ... 
doi:10.1287/opre.1110.0926 fatcat:wn4beswnbfgsfnjo4u6un5y5ue

StochKit-FF: Efficient Systems Biology on Multicore Architectures [chapter]

Marco Aldinucci, Andrea Bracciali, Pietro Liò, Anil Sorathiya, Massimo Torquati
2011 Lecture Notes in Computer Science  
StochKit-FF is based on the FastFlow programming toolkit for multicores and on the novel concept of selective memory.  ...  We present Stoch-Kit-FF, a parallel version of StochKit, a reference toolkit for stochastic simulations.  ...  Designing suitable high-level abstractions for parallel programming is a long standing problem [8] .  ... 
doi:10.1007/978-3-642-21878-1_21 fatcat:45jz43xphbhphdhzvtoqbbcqwa

Page 119 of Mathematical Reviews Vol. 24, Issue 1A [page]

1962 Mathematical Reviews  
Let Z denote the mean value of a numerical measurement on items in a random sample of size n from a lot submitted for inspection.  ...  A636 Variables sampling inspection for non-normal samples. J. Sci. Engrg. Res. 5 (1961), 145-152.  ... 

A simulation-based approach to two-stage stochastic programming with recourse

Alexander Shapiro, Tito Homem-de-Mello
1998 Mathematical programming  
In particular, we discuss in detail and present numerical results for two-stage stochastic programming with recourse where the random data have a continuous (multivariate normal) distribution.  ...  We think that the novelty of the numerical approach developed in this paper is twofold. First, various variance reduction techniques are applied in order to enhance the rate of convergence.  ...  Jfinos Mayer for providing test problems as well as solutions obtained with his software, and to two referees whose comments helped to improve the presentation of this paper.  ... 
doi:10.1007/bf01580086 fatcat:zn5ogbj2tngynje5epli7s3j6u

Page 3873 of Mathematical Reviews Vol. , Issue 86h [page]

1986 Mathematical Reviews  
Nevertheless, we show in this paper that there are two stochastic model reduction algorithms in the lit- erature which result in a deterministically balanced model.  ...  Programming 30 (1984), no. 3, 313-325.  ... 

Monte Carlo sampling-based methods for stochastic optimization

Tito Homem-de-Mello, Güzin Bayraksan
2014 Surveys in Operations Research and Management Science  
This paper surveys the use of Monte Carlo sampling-based methods for stochastic optimization problems.  ...  a stochastic optimization problem with sampling.  ...  Acknowledgments The authors express their gratitude to Sam Burer for the invitation to write this paper and for his infinite patience.  ... 
doi:10.1016/j.sorms.2014.05.001 fatcat:wxcytmx6urbyng6q2hsm3tmbtq

Multicore Architectures [chapter]

2010 Chapman & Hall/CRC Computational Science  
StochKit-FF is based on the FastFlow programming toolkit for multicores and exploits the novel concept of selective memory.  ...  We present StochKit-FF, a parallel version of StochKit, a reference toolkit for stochastic simulations.  ...  StochKit-FF main-HIV Model: average and variance for multiple trajectories (16x). Left to right: 1.  ... 
doi:10.1201/b10442-2 fatcat:kiokswgyrfdnvfjrnpf5gmbbda

StochKit-FF: Efficient Systems Biology on Multicore Architectures [article]

Marco Aldinucci and Andrea Bracciali and Pietro Liò and Anil Sorathiya and Massimo Torquati
2010 arXiv   pre-print
StochKit-FF is based on the FastFlow programming toolkit for multicores and exploits the novel concept of selective memory.  ...  We present StochKit-FF, a parallel version of StochKit, a reference toolkit for stochastic simulations.  ...  StochKit-FF main-HIV Model: average and variance for multiple trajectories (16x). Left to right: 1.  ... 
arXiv:1007.1768v1 fatcat:q4pblrksrraunot37rp4xfzmfq

Fast Variance Reduction Method with Stochastic Batch Size [article]

Xuanqing Liu, Cho-Jui Hsieh
2018 arXiv   pre-print
In addition, we also conduct a precise analysis to compare different update rules for variance reduction methods, showing that SAGA++ converges faster than SVRG in theory.  ...  In this paper we study a family of variance reduction methods with randomized batch size---at each step, the algorithm first randomly chooses the batch size and then selects a batch of samples to conduct  ...  Algorithm 1 Variance Reduction Method with Stochastic Batch Size Input: training samples {(x i , y i )} n i=1 , initial guess w 0 Output: w * = arg min w F (w) w = w 0 ; for iter= 0 to MAX ITER do Choose  ... 
arXiv:1808.02169v1 fatcat:lcxgqhy6c5fprczv7nn2brgsoy

Importance Sampling in Stochastic Programming: A Markov Chain Monte Carlo Approach

Panos Parpas, Berk Ustun, Mort Webster, Quang Kha Tran
2015 INFORMS journal on computing  
In this work, we introduce an importance sampling framework for stochastic programming that can produce accurate estimates of the recourse function using a small number of samples.  ...  Previous approaches for importance sampling in stochastic programming were limited to problems where the uncertainty was modeled using discrete random variables, and the recourse function was additively  ...  Importance sampling is just one of many variance reduction techniques that can be used in stochastic programming.  ... 
doi:10.1287/ijoc.2014.0630 fatcat:funx7sorkndvfixnzle5mdsu44

On Designing Multicore-Aware Simulators for Biological Systems

Marco Aldinucci, Mario Coppo, Ferruccio Damiani, Maurizio Drocco, Massimo Torquati, Angelo Troina
2011 2011 19th International Euromicro Conference on Parallel, Distributed and Network-Based Processing  
The stochastic simulation of biological systems is an increasingly popular technique in bioinformatics.  ...  We discuss the main opportunities to speed it up on multi-core platforms, which pose new challenges for parallelisation techniques.  ...  ACKNOWLEDGEMENTS We wish to thank Gianfranco Balbo for the many fruitful discussions on simulation techniques.  ... 
doi:10.1109/pdp.2011.81 dblp:conf/pdp/AldinucciCDDTT11 fatcat:hgjnkfzgnrcqnlxgdyasibhioq
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