Auflistung Stochastic Programming E-print Series (SPEPS) nach Titel
Anzeige der Publikationen 103-122 von 240
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2008-04-05BuchEpi-convergent scenario generation method for stochastic problems via sparse grid One central problem in solving stochastic programming problems is to generate moderate-sized scenario trees which represent well the risk faced by a decision maker. In this paper we propose an efficient scenario generation ...
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2006-03-09BuchEstimation method of multivariate exponential probabilities based on a simplex coordinates transform A novel unbiased estimator for estimating the probability mass of a multivariate exponential distribution over a measurable set is introduced and is called the Exponential Simplex (ES) estimator. For any measurable set, ...
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2003-07-14BuchEvaluation of scenario-generation methods for stochastic programming In this paper, we discuss the evaluation of quality/suitability of scenario-generation methods for a given stochastic programming model. We formulate minimal requirements that should be imposed on a scenario-generation ...
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2002-04-22BuchExact solutions to a class of stochastic generalized assignment problems This paper deals with a stochastic Generalized Assignment Problem with recourse. Only a random subset of the given set of jobs will require to be actually processed. An assignment of each job to an agent is decided a priori, ...
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2005-04-11BuchExtending algebraic modelling languages for Stochastic Programming The algebraic modelling languages (AML) have gained wide acceptance and use in Mathematical Programming by researchers and practitioners. At a basic level, stochastic programming models can be defined using these languages ...
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2009-05-22BuchFenchel Decomposition for Stochastic Mixed-IntegerProgramming This paper introduces a new cutting plane method for two-stage stochastic mixed-integer programming (SMIP) called Fenchel decomposition (FD). FD usesa class of valid inequalities termed, FD cuts, which are derived based ...
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2000-02-16BuchFinite capacity production planning with random demand and limited information Production planning has a fundamental role in any manufacturing operation. The problem is to decide what type of, and how much, product should be produced in future time periods. The decisions should be based on many ...
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2002-10-24BuchFrontiers of stochastically nondominated portfolios We consider the problem of constructing a portfolio of finitely many assets whose returns are described by a discrete joint distribution. We propose mean-risk models which are solvable by linear programming and generate ...
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2006-03-20BuchGenetic algorithm based technique for solving chance constrained problems Management and measurement of risk is an important issue in almost all areas that require decisions to be made under uncertain information. Chance Constrained Programming (CCP) have been used for modelling and analysis of ...
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2012-02-20BuchGradient 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 ...
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1999-12-20BuchHedging electricity portfolios via stochastic programming Electricity producers participating in the Nordic wholesale-level market face significant uncertainty in inflow to reservoirs and prices in the spot and contract markets. Taking the view of a single risk-averse producer, ...
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2002-05-03BuchHigher-Order Upper Bounds on the Expectation of a Convex Function We develop a decreasing sequence of upper bounds on the expectation of a convex function. The n-th term in the sequence uses moments and cross-moments of up to degree n from the underlying random vector. Our work has ...
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2003-03-06BuchHölder and Lipschitz Stability of Solution Sets in Programs with Probabilistic Constraints We study perturbations of a stochastic program with a probabilistic constraint and $r$-concave original probability distribution. First we improve our earlier results substantially and provide conditions implying Hölder ...
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2002-07-29BuchIntegrated chance constraints We consider integrated chance constraints (ICC), which provide quantitative alternatives for traditional chance constraints. We derive explicit polyhedral descriptions for the convex feasible sets induced by ICCs, for the ...
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2003-07-04BuchIntegrated chance constraints in an ALM model for pension funds We discuss integrated chance constraints in their role of short-term risk constraints in a strategic ALM model for Dutch pension funds. The problem is set up as a multi-stage recourse model, with special attention for ...
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2002-06-04BuchIntegration quadratures in discretization of stochastic programs Because of its simplicity, conditional sampling is the most popular method for generating scenario trees in stochastic programming. It is based on approximating probability measures by empirical ones generated by random ...
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2003-09-30BuchIntertemporal mean-variance efficiency with a Markovian state price density This paper extends Merton's continuous time (instantaneous) mean-variance analysis and the mutual fund separation theory. Given the existence of a Markovian state price density process, the optimal portfolios from concave ...
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2000-01-20BuchIntertemporal Surplus Management This paper presents an intertemporal portfolio selection model for pension funds that maximize the intertemporal expected utility of the surplus of assets net of liabilities. Following Merton (1973) it is assumed that both ...
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2012-06-08BuchIntroduction to convex optimization in financial markets Convexity arises quite naturally in financial risk management. In riskpreferences concerning random cash-flows, convexity corresponds to thefundamental diversification principle. Convexity is a basic property alsoof budget ...
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2002-11-05BuchLearning algorithms for separable approximations of stochastic optimization problems We propose the use of sequences of separable, piecewise linear approximations for solving classes of nondiffferential stochastic optimization problems. The approximations are estimated adaptively using a combination of ...