Stochastic Programming Eprint Series (SPEPS)
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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20170419BuchQuantitative Stability Analysis for Minimax Distributionally Robust RiskOptimization This paper considers distributionally robust formulations of a two stage stochastic programmingproblem with the objective of minimizing a distortion risk of the minimal cost incurred at the secondstage.We carry out stability ...

20170419BuchOptimal scenario generation and reduction in stochastic programming Scenarios are indispensable ingredients for the numerical solution of stochastic optimization problems. Earlier approaches for optimal scenario generation and reduction are based on stability arguments involving distances ...

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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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 ...

20141016BuchDynamic Generation of Scenario Trees We present new algorithms for the dynamic generation of scenario trees for multistagestochastic optimization. The different methods described are based on random vectors, whichare drawn from conditional distributions given ...

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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 ...

20130725BuchConditioning of linearquadratic twostage stochastic optimization problems In this paper a condition number for linearquadratic twostage stochastic optimization problemsis introduced as the Lipschitz modulus of the multifunction assigning to a (discrete) probabilitydistribution the solution set ...

20130724BuchBidding in sequential electricity markets: The Nordic case For electricity market participants trading in sequential markets with differences in price levels and riskexposure, coordinated bidding is highly relevant. We consider a Nordic power producer who engages inthe dayahead ...