Browsing Stochastic Programming Eprint Series (SPEPS) by Title
Now showing items 3655 of 240

20050428BuchAdaptive and nonadaptive samples in solving stochastic linear programs A computational investigationLarge scale stochastic linear programs are typically solved using a combination of mathematical programming techniques and samplebased approximations. Some methods are designed to permit sample sizes to adapt to information ...

20001107BuchAdaptive optimal stochastic trajectory planning and control (AOSTPC) for robots In optimal control of robots, the standard procedure is to determine first offline an optimal openloop control, using some nominal or estimated values of the model parameters, and to correct then the resulting deviation ...

20051228BuchAggregation 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, ...

20061214BuchAirline 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 ...

20070810BuchAlgorithms for handling CVaRconstraints in dynamic stochastic programming models with applications to finance We propose dual decomposition and solution schemes for multistage CVaRconstrained problems. These schemes meet the need for handling multiple CVaRconstraints for different time frames and at different confidence levels. ...

20050830BuchAmbiguous 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 ...

20090422BuchAn enhanced model for portfolio choice with SSD criteria: a constructive approach We formulate a portfolio planning model which is based on Secondorder Stochastic Dominance as the choice criterion. This model is an enhanced version of the multiobjective model proposed by Roman, DarbyDowman, and Mitra ...

20030721BuchAn Ergodic Theorem for Random Lagrangians with an Application to Stochastic Programming We prove an ergodic theorem showing the almost sure epi/hypoconvergence of a sequence of random lagrangians to a limit lagrangian where the random lagrangians are generated by stationary sampling of a probability measure. ...

20070529BuchAn Exact Solution Approach for Portfolio Optimization Problems under Stochastic and Integer Constraints In this paper, we study extensions of the classical Markowitz’ meanvariance portfolio optimization model. First, we consider that the expected asset returns are stochastic by introducing aprobabilistic constraint imposing ...

20130917BuchAncestral Benders' Cuts and Multiterm Disjunctions for MixedInteger Recourse Decisions in Stochastic Programming This paper focuses on solving twostage 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 ...

20020705BuchApplying 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 ...

20010626BuchApplying 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 ...

20030630BuchApproximation 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 ...

20080916BuchApproximations 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 ...

20040521BuchArbitrage 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 wellknown ...

20030930BuchArbitrage 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, ...

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 ...

20040517BuchAssessing policy quality in multistage stochastic programming Solving a multistage stochastic program with a large number of scenarios and a moderatetolarge number of stages can be computationally challenging. We develop two Monte Carlobased methods that exploit special structures ...

20050225BuchAssessing 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 samplingbased procedures for assessing ...

20040114BuchAssetliability 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 ...