Browsing Stochastic Programming Eprint Series (SPEPS) by Title
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20151016BuchParallel 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 ...

20100825BuchPatternBased Modeling and Solution of Probabilistically Constrained Optimization Problems We propose a new modeling and solution method for probabilistically constrained optimization problems.The methodology is based on the integration of the stochastic programming and combinatorialpattern recognition fields. ...

20030210BuchPerturbation ananlysis of chanceconstrained programs under variation of all constraint data A fairly general shape of chance constraint programs is\[(P) min \{ g(x)  x \in X, \mu (H(x)) \le p \} ,\]where $g : \R^m \to \R$ is a continuous objective function, $X \subseteq \R^m$ is a closed subset of deterministic ...

20040605BuchPolyhedral inclusionexclusion Motivated by numerical computations to solve probabilistic constrained stochastic programming problems, we derive a new identity claiming that many terms are cancelled out in the inclusionexclusion formula expressing the ...

20030930BuchPortfolio optimization with stochastic dominance constraints We consider the problem of constructing a portfolio of finitely many assets whose returns are described by a discrete joint distribution. We propose a new portfolio optimization model involving stochastic dominance constraints ...

20000120BuchPower management in a hydrothermal system under uncertainty by Lagrangian relaxation We present a dynamic multistage stochastic programming model for the costoptimal generation of electric power in a hydrothermal system under uncertainty in load, inflow to reservoirs and prices for fuel and delivery ...

20001005BuchProbabilistic programs with discrete distributions and precedence constrained knapsack polyhedra We consider stochastic programming problems with probabilistic constraints involving random variables with discrete distributions. They can be reformulated as large scale mixed integer programming problems with knapsack ...

20080702BuchProcessing SecondOrder Stochastic Dominance models using cuttingplane representations Secondorder stochastic dominance (SSD) is widely recognised as an important decision criteria in portfolio selection. Unfortunately, stochastic dominance models can be very demanding from a computational point of view. ...

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 ...

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 ...

20001220BuchQuantitative stability in stochastic programming The method of probability metricsQuantitative stability of optimal values and solution sets to stochastic programming problems is studied when the underlying probability distribution varies in some metric space of probability measures. We give conditions ...

20071207BuchQuantitative stability of fully random mixedinteger twostage stochastic programs Mixedinteger twostage stochastic programs with ﬁxed recourse matrix, random recourse costs, technology matrix, and righthand sides areconsidered. Quantitative continuity properties of its optimal value and solution set ...

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 ...

20000321BuchRandom lsc functions ScalarizationRandom lsc (lower semicontinuous) functions can be indentified with a vectorvalued random variable by means of an appropriate scalarization. It is shown that stationarity, ergodicity and independence properties are preserved ...

20000207BuchRandom lsc functions An ergodic theoremAn ergodic theorem for random lsc functions is obtained by relying on a (novel) 'scalarization' of such functions. In the process, Kolmogorov's extension theorem for randon lsc functions is established. Applications to ...

20100604BuchReformulation of general chance constrained problems using the penalty functions We explore reformulation of nonlinear stochastic programs with several joint chance constraints by stochastic programs with suitably chosenpenaltytype objectives. We show that the two problems are asymptotically equivalent. ...

20020816BuchRisk aversion via excess probabilities in stochastic programs with mixedinteger recourse We consider linear twostage stochastic programs with mixedinteger recourse. Instead of basing the selection of an optimal firststage solution on expected costs alone, we include into the objective a risk term reflecting ...

20011004BuchRisk measures for income streams A new measure of risk is introduced for a sequence of random incomes adapted to some filtration. This measure is formulated as the optimal net present value of a stream of adaptively planned commitments for consumption. ...

20150916BuchRisk measures for vectorvalued returns Portfolios, which are exposed to different currencies, have separate and different returns ineach individual currency and are thus vectorvalued in a natural way.This paper investigates the natural domain of these risk ...

20091016BuchRiskAverse TwoStage Stochastic LinearProgramming: Modeling and Decomposition We formulate a riskaverse twostage stochastic linear programming problem in which unresolved uncertainty remains after the second stage. The objective function is formulated as a composition of conditional risk measures.We ...