Browsing Stochastic Programming E-print Series (SPEPS) by Title
Now showing items 36-55 of 240
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2005-04-28BuchAdaptive and nonadaptive samples in solving stochastic linear programs Large scale stochastic linear programs are typically solved using a combination of mathematical programming techniques and sample-based approximations. Some methods are designed to permit sample sizes to adapt to information ...
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2000-11-07BuchAdaptive optimal stochastic trajectory planning and control (AOSTPC) for robots In optimal control of robots, the standard procedure is to determine first off-line an optimal open-loop control, using some nominal or estimated values of the model parameters, and to correct then the resulting deviation ...
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2005-12-28BuchAggregation and Discretization in Multistage Stochastic Programming Multistage stochastic programs have applications in many areas and support policy makers in finding rational decisions that hedge against unforeseen neg- ative events. In order to ensure computational tractability, ...
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2006-12-14BuchAirline Network Revenue Management by Multistage Stochastic Programming A multistage stochastic programming approach to airline network revenue management is presented. The objective is to determine seatprotection levels for all itineraries, fare classes, point of sales of the airlinenetwork ...
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2007-08-10BuchAlgorithms for handling CVaR-constraints in dynamic stochastic programming models with applications to finance We propose dual decomposition and solution schemes for multistage CVaR-constrained problems. These schemes meet the need for handling multiple CVaR-constraints for different time frames and at different confidence levels. ...
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2005-08-30BuchAmbiguous chance constrained problems and robust optimization In this paper we study ambiguous chance constrained problems where the distributions of the random parameters in the problem are themselves uncertain. We focus primarily on the special case where the uncertainty set Q of ...
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2009-04-22BuchAn enhanced model for portfolio choice with SSD criteria: a constructive approach We formulate a portfolio planning model which is based on Second-order Stochastic Dominance as the choice criterion. This model is an enhanced version of the multi-objective model proposed by Roman, Darby-Dowman, and Mitra ...
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2003-07-21BuchAn Ergodic Theorem for Random Lagrangians with an Application to Stochastic Programming We prove an ergodic theorem showing the almost sure epi/hypo-convergence of a sequence of random lagrangians to a limit lagrangian where the random lagrangians are generated by stationary sampling of a probability measure. ...
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2007-05-29BuchAn Exact Solution Approach for Portfolio Optimization Problems under Stochastic and Integer Constraints In this paper, we study extensions of the classical Markowitz’ mean-variance portfolio optimization model. First, we consider that the expected asset returns are stochastic by introducing aprobabilistic constraint imposing ...
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2013-09-17BuchAncestral Benders' Cuts and Multi-term Disjunctions for Mixed-Integer Recourse Decisions in Stochastic Programming This paper focuses on solving two-stage stochastic mixed integer programs (SMIPs) with general mixed integer decision variables in both stages. We develop a decomposition algorithm in which the first stage approximation ...
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2002-07-05BuchApplying the minimax criterion in stochastic recourse programs We consider an optimization problem in which some uncertain parmeters are replaced by random variables. The minimax approach to stochastic programming concerns the problem of minimizing the worst expected value of the ...
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2001-06-26BuchApplying the minimum risk criterion in stochastic recourse programs In the setting of stochastic recourse programs, we consider the problem of minimizing the probability of total costs exceeding a certain threshold value. The problem is referred to as the minimum risk problem and is posed ...
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2003-06-30BuchApproximation in stochastic integer programming Approximation algorithms are the prevalent solution methods in the field of stochastic programming. Problems in this field are very hard to solve. Indeed, most of the research in this field has concentrated on designing ...
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2008-09-16BuchApproximations and contamination bounds for probabilistic programs In this paper we aim at output analysis with respect to changes of the probability distribution for problems with probabilistic (chance) constraints. The perturbations are modeled via contamination of the initial probability ...
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2004-05-21BuchArbitrage pricing of American contingent claims in incomplete markets - a convex optimization approach Convex optimization provides a natural framework for pricing and hedging financial instruments in incomplete market models. Duality theory of convex optimization has been shown to yield elementary proofs of well-known ...
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2003-09-30BuchArbitrage pricing simplified The paper derives fundamental arbitrage pricing results in finite dimensions in a simple unified framework using Tucker's theorem of the alternative. Frictionless results plus those with dividends, periodic interest payments, ...
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2012-09-24BuchAre Quasi-Monte Carlo algorithms efficient for two-stage stochastic programs? Quasi-Monte Carlo algorithms are studied for designing discrete approximationsof two-stage linear stochastic programs. Their integrands are piecewiselinear, but neither smooth nor lie in the function spaces considered for ...
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2004-05-17BuchAssessing policy quality in multi-stage stochastic programming Solving a multi-stage stochastic program with a large number of scenarios and a moderate-to-large number of stages can be computationally challenging. We develop two Monte Carlo-based methods that exploit special structures ...
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2005-02-25BuchAssessing Solution Quality in Stochastic Programs Determining whether a solution is of high quality (optimal or near optimal) is a fundamental question in optimization theory and algorithms. In this paper, we develop Monte Carlo sampling-based procedures for assessing ...
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2004-01-14BuchAsset-liability management for Czech pension funds using stochastic programming It is possible to model a wide range of portfolio management problems using stochastic programming. This approach requires the generation of input scenarios and probabilities, which represent the evolution of the return ...