Stochastic Programming E-print Series (SPEPS)
1999 - 2018
The Stochastic Programming E-Print 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.
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 error-free. Thus, the authors are encouraged to submit their paper to an archival journal.
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 e-mail 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 e-mail should be adressed to one of the co-editors (Sen or Römisch).
Unlike an archival journal, this E-Print 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.
Suvrajeet S e n
University of Southern California, Los Angeles
Werner R ö m i s c h
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. J-B W e t s, University of California, Davis
W. T. Z i e m b a, University of British Columbia, Vancouver
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, Gerhard-Mercator-University 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
Stochastic Programming Links
Stochastic Programming Bibliography
Stochastic Programming Community Home Page
COSP e-mail list
2017-09-26BuchA 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 ...
2017-07-31BuchLearning 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 ...
2017-04-19BuchQuantitative 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 ...
2017-04-19BuchOptimal 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 ...
2017-02-21BuchScenariao 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, ...
2016-09-05BuchUniformly monotone functions - defiitions, properties, characterizations Quasi-concave functions play an important role in economics and finance as utility functions, measures of risk or other objects used, mainly,in portfolio selection analysis. A special attention is paid to these functions ...
2015-10-05BuchClustering of sample average approximation for stochastic program We present an improvement to the Sample Average Approximation (SAA) method for two-stage stochasticprogram. Although the SAA has nice theoretical properties, such as convergence in probability and consistency,as long as ...
2015-09-16BuchRisk measures for vector-valued returns Portfolios, which are exposed to different currencies, have separate and different returns ineach individual currency and are thus vector-valued in a natural way.This paper investigates the natural domain of these risk ...
2015-10-16BuchParallel stochastic optimization based on descent algorithms This study addresses the stochastic optimization of a function unknown in closed form which can only be estimated based on measurementsor simulations. We consider parallel implementations of a class of stochasticoptimization ...
2015-09-14BuchConvergence of the Smoothed Empirical Process in Nested Distance The nested distance, also process distance, provides a quantitative measure of distance for stochastic processes. It is the crucial and determining distance for stochastic optimization problems.In this paper we demonstrate ...
2015-05-12BuchStatistical 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 ...
2015-04-22BuchA Comment on "Computational Complexityof Stochastic Programming Problems" Although stochastic programming problems were always believed to be computationally chal-lenging, this perception has only recently received a theoretical justification by the seminal workof Dyer and Stougie (Mathematical ...
2015-04-09BuchA 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 ...
2014-12-30BuchQuasi-Monte Carlo methods for linear two-stage stochastic programming problems Quasi-Monte Carlo algorithms are studied for generating scenarios to solve two-stage linear stochastic programming problems. Their integrands are piecewise linear-quadratic, but do not belong to the function spaces ...
2014-12-30BuchDistribution 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 ...
2014-10-16BuchDynamic 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 ...
2014-05-07BuchOn Distributionally Robust Multiperiod Stochastic Optimization This paper considers model uncertainty for multistage stochastic programs. The data and information structure of the baseline model is a tree, on which the decision problem is defined. We consider ambiguity neighborhoods ...
2014-04-16BuchMitigating Uncertainty via Compromise Decisions in Two-stage Stochastic Linear Programming Stochastic Programming (SP) has long been considered as a well-justified yet computationally challenging paradigm for practical applications. Computational studies in the literature often involve approximating a large ...
2014-04-04BuchMulti-Objective Probabilistically Constrained Programming with Variable Risk: New Models and Applications We consider a class of multi-objective probabilistically constrained problems MOPCP with a joint chance constraint, a multi-row random technology matrix, and a risk parameter (i.e., the reliability level) defined as a ...
2013-09-17BuchAncestral Benders' Cuts and Multi-term Disjunctions for Mixed-Integer Recourse Decisions in Stochastic Programming This paper focuses on solving two-stage 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 ...