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Variance Reduction for Sequential Sampling in Stochastic Programming
[article]
2020
arXiv
pre-print
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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
2011
Proceedings of the 2011 Winter Simulation Conference (WSC)
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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]
2009
Decision Technologies and Applications
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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
2014
2014 Power Systems Computation Conference
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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
2011
Operations Research
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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]
2011
Lecture Notes in Computer Science
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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
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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
1998
Mathematical programming
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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
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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
2014
Surveys in Operations Research and Management Science
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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
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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]
2010
arXiv
pre-print
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The file type is application/pdf.
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]
2018
arXiv
pre-print
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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
2015
INFORMS journal on computing
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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
2011
2011 19th International Euromicro Conference on Parallel, Distributed and Network-Based Processing
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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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