Titlebook: Stochastic Optimization; Algorithms and Appli Stanislav Uryasev,Panos M. Pardalos Book 2001 Springer Science+Business Media Dordrecht 2001

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A Finite-Dimensional Approach to Infinite-Dimensional Constraints in Stochastic Programming Dualityiding a new framework for problems which do not necessarily have relatively complete recourse and do not satisfy the typical Slater conditions. The results instead rely on the much weaker constraint qualification of ‘calmness’ of certain finite-dimensional marginal functions to derive the existence

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Non-Linear Risk of Linear Instruments,tion leading to a Normal approximation of the profit and loss distribution. In this note we consider the density of the distribution of the change in value of a zero coupon bond. We describe all possible shapes of the density function assuming that log-returns of the risk factor have a normal distri

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Multialgorithms for Parallel Computing: A New Paradigm for Optimization,uence of traditional optimization techniques, evolutionary algorithms and parallel computing..A detailed illustration is given within the context of a new two-parameter family of nonlinear conjugate gradient algorithms and a set of four standard test problems..The illustration provides a platform fo

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Convergence Rate of Incremental Subgradient Algorithms,pe of minimization arises in a dual context from Lagrangian relaxation of the coupling constraints of large scale separable problems. The idea is to perform the subgradient iteration incrementally, by sequentially taking steps along the subgradients of the component functions, with intermediate adju

FLING

Transient Stochastic Models for Search Patterns,h effort in a manner that minimizes the length of time to target detection. This is especially important in the use of autonomous searchers with the capability to locate and identify targets. For our current application, we assume that the searcher can only engage a single target. The use of transie

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Value-at-Risk Based Portfolio Optimization, by nature measuring the probability of worst case portfolio performance. In this paper I present four model frameworks that apply VaR to . portfolio decisions. The mean-variance model, Young’s (1998) minimax model and Hiller and Eckstein’s (1993) stochastic programming model are extended to incorpo

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Combinatorial Optimization, Cross-Entropy, Ants and Rare Events, auxiliary random mechanism, like a Markov chain, which converts the original deterministic network into an associated stochastic one, called the . (ASN). Depending on a particular problem, we introduce the randomness in ASN by making either the nodes or the edges of the network random. Each iterati

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Consistency of Statistical Estimators: the Epigraphical View,lity measures are jointly considered to analyze the asymptotic behaviour of statistical functionals. The twofold role is key in deriving consistency for a wide class of statistical estimators which includes most of the cases of interest.