Stochastic Programming Eprint Series (SPEPS): Recent submissions
Now showing items 2140 of 240

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 ...

20130514BuchA mixedinteger stochastic nonlinear optimization problem with joint probabilistic constraints We illustrate the solution of a mixedinteger stochastic nonlinear optimization problem in an application of power management. In this application, a coupled system consisting of a hydro power station and a wind farm is ...

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 ...

20130409BuchComputational aspects of riskaverse optimizationin twostage stochastic models Computational studies on twostage stochastic programming problems indicate that aggregate models have better scaleup properties than disaggregate ones, though the threshold of breaking even may be high. In this paper we ...

20130409BuchThe Natural Banach Space for Version Independent Risk Measures Risk measures, or coherent measures of risk are often considered on the space $L^\infty$, andimportant theorems on risk measures build on that space. Other risk measures, among themthe most important risk measure – the ...

20130402BuchConvex approximations for totally unimodular integerrecourse models: A uniform error bound We consider a class of convex approximations for totally unimodular (TU) integer recourse models and derive a uniform error bound by exploiting properties of the total variation of the probability density functions involved. ...

20121221BuchConvex hull approximation of TU integer recourse models:Counterexamples, sufficient conditions, and special cases We consider a convex approximation for integer recourse models. In particular, we showthat the claim of Van der Vlerk (2004) that this approximation yields the convex hull of totallyunimodular (TU) integer recourse models ...

20121123BuchThreshold Boolean Form for Joint Probabilistic Constraints with Random Technology Matrix We develop a new modeling and exact solution method for stochastic programming problems thatinclude a joint probabilistic constraint in which the multirow random technology matrix is discretely distributed. We binarize ...

20121031BuchOptimizing existing railway timetables by means of stochastic programming We present some models to find the best allocation of a limited amount of socalled runningtime supplements (extra minutes added to a timetable to reduce delays) on a railway line. Bythe best allocation, we mean the solution ...

20120608BuchIntroduction to convex optimization in financial markets Convexity arises quite naturally in financial risk management. In riskpreferences concerning random cashflows, convexity corresponds to thefundamental diversification principle. Convexity is a basic property alsoof budget ...

20121013BuchQuantitative Stability Analysis of Stochastic Generalized Equations We consider the solution of a system of stochastic generalized equations (SGE) where theunderlying functions are mathematical expectation of random setvalued mappings. SGE hasmany applications such as characterizing ...

20120924BuchAre QuasiMonte Carlo algorithms efficient for twostage stochastic programs? QuasiMonte Carlo algorithms are studied for designing discrete approximationsof twostage linear stochastic programs. Their integrands are piecewiselinear, but neither smooth nor lie in the function spaces considered for ...

20120409BuchSDDP for multistage stochastic linear programs based on spectral risk measures We consider riskaverse formulations of multistage stochastic linear programs. Forthese formulations, based on convex combinations of spectral risk measures, riskaverse dynamicprogramming equations can be written. As a ...

20120319BuchMultistage Stochastic Decomposition: A Bridge between Stochastic Programming and Approximate Dynamic Programming Multistage stochastic programs (MSP) pose some of the more challenging optimizationproblems. Because such models can become rather intractable in general, it is important todesign algorithms that can provide approximations ...

20120319BuchMeasures of information in multistage stochastic programming(Bounds in Multistage Linear Stochastic Programming) Multistage stochastic programs, which involve sequences of decisions over time, areusually hard to solve in realistically sized problems. In the twostage case, several approaches basedon different levels of available ...

20120220BuchGradient estimates for Gaussian distribution functions: Application to probabilistically constrained optimization problems We provide lower estimates for the norm of gradients of Gaussian distribution functions and apply the results obtained to a special class ofprobabilistically constrained optimization problems. In particular, it is shown ...

20111128BuchOn the Geometry of Acceptability Functionals In this paper we discuss the geometry of acceptability functionals or risk measures. The dependenceof the random variable is investigated ﬁrst. The main contribution and focus of this paper is to studyhow acceptability ...

20111128BuchMultistage Optimization We provide a new identity for the multistage Average ValueatRisk. The identity is based on the conditional Average ValueatRisk at random level, which is introduced. It is of interest in situations, where the information ...

20110913BuchA gradient formula for linear chance constraints under Gaussian distribution We provide an explicit gradient formula for linear chance constraints under a (possibly singular) multivariate Gaussian distribution. This formula allows one to reduce the calculus of gradients to the calculus of values ...