Volume 2012
Recent Submissions

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 ...