Browsing Stochastic Programming Eprint Series (SPEPS) by Title
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20080307BuchDantzigWolfe decomposition for solving multistage stochastic capacityplanning problems We describe a multistage, stochastic, mixedintegerprogramming model for planning discrete capacity expansion of production facilities. A scenario tree represents uncertainty in the model; a general mixedinteger program ...

20091016BuchDayAhead Market Bidding for a NordicHydropower Producer: Taking the ElbasMarket into Account In many power markets around the world the energy generation decisions result from twosidedauctions in which producing and consuming agents submit their pricequantity bids. Thedetermination of optimal bids in power markets ...

20051229BuchDecomposing CVaR minimization in twostage stochastic models Based on the polyhedral representation of KünziBay and Mayer (2005), we propose a decomposition framework for the minimization of CVaR in twostage stochastic models.We show that the decomposed problems can be effectively ...

20010526BuchDecomposition algorithms for stochastic programming on a computational grid We describe algorithms for twostage stochastic linear programming with recourse and their implementation on a grid computing platform. In particular, we examine serial and asynchronous versions of the Lshaped method and ...

20070805BuchDecomposition of Multistage Stochastic Programs with Recombining Scenraio Trees This paper presents a decomposition approach for linear multistage stochasticprograms, that is based on the concept of recombining scenario trees. The latter, widely applied in Mathematical Finance, may prevent the node ...

20010130BuchDecomposition of test sets in stochastic integer programming Graver test sets for linear twostage stochastic integer programs are studied. It is shown that test sets can be decomposed into finitely many building blocks whose number is independent of the number of scenarios of the ...

20040420BuchDecompositionbased interior point methods for twostage stochastic convex quadratic programs with recourse Zhao [28] recently showed that the log barrier associated with the recourse function of twostage stochastic linear programs behaves as a strongly selfconcordant barrier and forms a self concordant family on the first ...

20080317BuchDeltaHedging a Hydropower Plant Using Stochastic Programming An important challenge for hydropower producers is to optimize reservoir discharges, which is subject to uncertainty in inﬂow and electricity prices. Furthermore, the producers want to hedge the risk in the operating proﬁt. ...

20000613BuchDetermining risk neutral probabilities and optimal portfolio policies in a dynamic investment model with downside risk control in the presence of trading frictions This paper develops an approximate method for solving multiperiod utility maximization investment models with downside risk control characterized by the minimum attainable wealth among all possible scenarios. The stochastic ...

20080222BuchDisjunctive decomposition for twostage stochastic mixedbinary programs with random recourse This paper introduces disjunctive decomposition for twostage mixed 01 stochastic integer programs (SIPs) with random recourse. Disjunctive decomposition allows for cutting planes based on disjunctive programming to be ...

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 ...

20030930BuchDual effect free stochastic controls In stochastic optimal control, a key issue is the fact that "solutions" are searched for in terms of "feedback" over available information and, as a consequence, a major potential difficulty is the fact that present control ...

20010324BuchDual stochastic dominance and related meanrisk models We consider the problem of constructing meanrisk models which are consistent with the second degree stochastic dominance relation. By exploiting duality relations of convex analysis we develop the quantile model of ...

20020707BuchDuality gaps in nonconvex stochastic optimization We consider multistage stochastic optimization models. Logical or integrality constraints, frequently present in optimization models, limit the application of powerful convex analysis tools. Different Lagrangian relaxation ...

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 ...

20030517BuchDynamic splitting An algorithm for deterministic and stochastic multiperiod optimizationA new algorithm for the nonlinear multistage stochastic programming problem (MSP) is presented; one that is reasonable for the largescale problem (e.g. long term hydropower scheduling) and is highly parallel. The algorithm ...

20031020BuchEfficient point methods for probabilistic optimization problems We consider nonlinear stochastic programming problems with probabilistic constraints. The concept of a pefficient point of a probability distribution is used to derive equivalent problem formulations, and necessary and ...

20130513BuchElectricity Swing Option Pricing by Stochastic Bilevel Optimization: a Survey and New Approaches We demonstrate how the problem of determining the ask price for electricityswing options can be considered as a stochastic bilevel program with asymmetricinformation. Unlike as for financial options, there is no way for ...

20040210BuchEpiconvergent discretizations of multistage stochastic programs In many dynamic stochastic optimization problems in practice, the uncertain factors are best modeled as random variables with an infinite support. This results in infinitedimensional optimization problems that can rarely ...

20030725BuchEpiconvergent discretizations of stochastic programs via integration quadratures Modern integration quadratures are designed to produce finitely supported approximations of a given (probability) measure. This makes them well suited for discretization of stochastic programs. We give conditions that ...