Stochastic Programming Eprint Series (SPEPS)
1999  2018
EDITORIAL STATEMENT
The Stochastic Programming EPrint Series (SPEPS) is intended to serve as an online repository of recent results in the area of Stochastic Programming (SP). By SP, we mean decision and control models in which data evolves over time, and are subject to significant uncertainty.
GOALS
The goal of SPEPS is to provide a service to the SP community by managing rapid dissemination of results in the SP area. In general, papers will be screened (reviewed lightly), and will be either accepted or rejected based on the reports. That is, SPEPS will typically not consider multiple rounds of reviews. However, in those rare cases in which we suggest that the authors do submit a revised manuscript, the suggested revisions will be minimal. In keeping with these goals, the review/screening process for SPEPS will take no longer than three months from the date of submission. Furthermore, this process is not stringent enough for a paper to be considered errorfree. Thus, the authors are encouraged to submit their paper to an archival journal.
SUBMISSION
All papers will be submitted electronically, in either pdf or ps format. Only one file should be submitted, and it should contain a title page listing the title of the paper, the authors, and the email addresses of all authors. The pdf/ps file must be sent as an attachement to a "cover letter" expressing an interest in posting it on SPEPS. This email should be adressed to one of the coeditors (Sen or Römisch).
COPYRIGHT
Unlike an archival journal, this EPrint series will post accepted papers on this web site for four years from acceptance or until they are accepted for publication in a journal, and copyright is transferred from the authors to the publisher. At that point, it will be incumbent upon the authors to inform the Editorial Assistant to retire the paper from SPEPS. Failure to do so is a violation of SPEPS policies, and the editorial board will not be responsible for any copyright violations resulting from such negligence. By posting a paper, the author is consenting to have any subscriber of SPEPS download the paper for his/her use. However, papers downloaded from this site are not to be distributed without the consent of the author.
EDITORIAL BOARD
CoEditors
Suvrajeet S e n
University of Southern California, Los Angeles
s.sen@usc.edu
Werner R ö m i s c h
HumboldtUniversity Berlin
romisch@math.huberlin.de
Advisory Editors
M. A. H. D e m p s t e r, University of Cambridge
J. D u p a c o v a, Charles University, Prague
Y. E r m o l i e v, IIASA, Laxenburg
P. K a l l, University of Zurich
A. P r e k o p a, Rutgers University, New Brunswick
S. M. R o b i n s o n, University of Wisconsin, Madison
R. T. R o c k a f e l l a r, University of Washington, Seattle
R. JB W e t s, University of California, Davis
W. T. Z i e m b a, University of British Columbia, Vancouver
Associate Editors
J. R. B i r g e, The University of Chicago
R. H e n r i o n, Weierstrass Institute Berlin
A. J. K i n g, IBM, Yorktown Heights
K. M a r t i, Federal Armed Forces University, Munich
J. M a y e r, University of Zurich
V. I. N o r k i n, Glushkov Institute of Cybernetics, Kiev
G. P f l u g, University of Vienna
A. R u s z c z y n s k i, Rutgers University, New Brunswick
R. S c h u l t z, GerhardMercatorUniversity Duisburg
A. S h a p i r o, Georgia Institute of Technology, Atlanta
St. U r y a s e v, University of Florida, Gainesville
M.H. van der V l e r k, University of Groningen
S. W. W a l l a c e, Molde University College, Molde
Huifu X u, City University of London
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20170926BuchA randomized method for handling a difficult function in a convex optimization problem, motivated by probabilistic programming We propose a randomized gradient method for the handling of a convex function whose gradient computation is demanding. The method bears a resemblance to the stochastic approximation family. But in contrast to stochastic ...

20170731BuchLearning Enabled Optimization: Towards a Fusion of Statistical Learning and Stochastic Optimization Several emerging applications, such as “Analytics of Things" and “Integrative Analytics" call for a fusion of statistical learning (SL) and stochastic optimization (SO). The Learning Enabled Optimization paradigm fuses ...

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20170221BuchScenariao Reduction Revisited: Fundamental Limits and Gurarantees The goal of scenario reduction is to approximate a given discrete distributionwith another discrete distribution that has fewer atoms. We distinguishcontinuous scenario reduction, where the new atoms may be chosen freely, ...

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20150512BuchStatistical 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 ...

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20150409BuchA 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 ...

20141230BuchQuasiMonte Carlo methods for linear twostage stochastic programming problems QuasiMonte Carlo algorithms are studied for generating scenarios to solve twostage linear stochastic programming problems. Their integrands are piecewise linearquadratic, but do not belong to the function spaces ...

20141230BuchDistribution shaping and scenario bundling for stochastic programs with endogenous uncertainty Stochastic programs are usually formulated with probability distributions that are exogenously given. Modeling and solving problems withendogenous uncertainty, where decisions can influence the probabilities, has remained ...

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20140416BuchMitigating Uncertainty via Compromise Decisions in Twostage Stochastic Linear Programming Stochastic Programming (SP) has long been considered as a welljustified yet computationally challenging paradigm for practical applications. Computational studies in the literature often involve approximating a large ...

20140404BuchMultiObjective Probabilistically Constrained Programming with Variable Risk: New Models and Applications We consider a class of multiobjective probabilistically constrained problems MOPCP with a joint chance constraint, a multirow random technology matrix, and a risk parameter (i.e., the reliability level) defined as a ...

20130917BuchAncestral Benders' Cuts and Multiterm Disjunctions for MixedInteger Recourse Decisions in Stochastic Programming This paper focuses on solving twostage stochastic mixed integer programs (SMIPs) with general mixed integer decision variables in both stages. We develop a decomposition algorithm in which the first stage approximation ...