Auflistung Stochastic Programming E-print Series (SPEPS) nach Titel
Anzeige der Publikationen 152-171 von 240
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2004-12-27BuchOn the Fortet-Mourier metric for the stability of Stochastic Optimization Problems, an example We consider the use of the Fortet-Mourier metric between two probability measures to bound the error term made by an approximated solution of a stochastic program. After a short analysis of usual stability arguments, we ...
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2011-11-28BuchOn 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 ...
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2006-03-20BuchOn two-stage convex chance constrained problems In this paper we develop approximation algorithms for two-stage convex chance constrainedproblems. Nemirovski and Shapiro [16] formulated this class of problems and proposed anellipsoid-like iterative algorithm for the ...
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2006-10-18BuchOptimal Hedging Strategies for Multi-periodGuarantees in the Presence of Transaction Costs:A Stochastic Programming Approach Multi-period guarantees are often embedded in life insurance contracts. In this paper we consider the problem of hedging these multi-period guarantees in the presence of transaction costs. We derive thehedging strategies ...
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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 ...
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2003-06-18BuchOptimality and duality theory for stochastic optimization problems with nonlinear dominance constraints We consider a new class of optimization problems involving stochastic dominance constraints of second order. We develop a new splitting approach to these models, optimality conditions and duality theory. These results are ...
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2004-03-25BuchOptimization of Convex Risk Functions We consider optimization problems involving convex risk functions. By employing techniques of convex analysis and optimization theory in vector spaces of measurable functions we develop new representation theorems for risk ...
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2002-05-23BuchOptimization of simultaneous power production and trading by stochastic integer programming We develop a two-stage stochastic integer programming model for the simultaneous optimization of power production and day-ahead power trading. The model rests on mixed-integer linear formulations for the unit commitment ...
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2003-06-18BuchOptimization with stochastic dominance constraints We introduce stochastic optimization problems involving stochastic dominance constraints. We develop necessary and sufficient conditions of optimality and duality theory for theses models and we show that the Lagrange ...
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2000-05-24BuchOptimizing electricity distribution using two-stage integer recourse models We consider two planning problems faced by an electricity distributor. Electricity can be obtained both from power plants and small generators such as hospitals and greenhouses, whereas the future demand for electricity ...
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2012-10-31BuchOptimizing existing railway timetables by means of stochastic programming We present some models to find the best allocation of a limited amount of so-called runningtime supplements (extra minutes added to a timetable to reduce delays) on a railway line. Bythe best allocation, we mean the solution ...
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2000-07-06BuchOutput analysis for approximated stochastic programs Because of incomplete information and also for the sake of numerical tractability one mostly solves an approximated stochastic program instead of the underlying ''true'' decision problem. However, without an additional ...
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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 ...
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2010-08-25BuchPattern-Based 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. ...
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2003-02-10BuchPerturbation ananlysis of chance-constrained 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 ...
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2004-06-05BuchPolyhedral inclusion-exclusion 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 inclusion-exclusion formula expressing the ...
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2003-09-30BuchPortfolio 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 ...
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2000-01-20BuchPower management in a hydro-thermal system under uncertainty by Lagrangian relaxation We present a dynamic multistage stochastic programming model for the cost-optimal generation of electric power in a hydro-thermal system under uncertainty in load, inflow to reservoirs and prices for fuel and delivery ...
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2000-10-05BuchProbabilistic 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 ...
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2008-07-02BuchProcessing Second-Order Stochastic Dominance models using cutting-plane representations Second-order 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. ...