Auflistung Stochastic Programming E-print Series (SPEPS) nach Titel
Anzeige der Publikationen 121-140 von 240
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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 ...
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2017-07-31BuchLearning Enabled Optimization: Towards a Fusion of Statistical Learning and Stochastic Optimization Several emerging applications, such as “Analytics of Things" and “Integrative Analytics" call for a fusion of statistical learning (SL) and stochastic optimization (SO). The Learning Enabled Optimization paradigm fuses ...
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2005-04-26BuchLipschitz and differentiability properties of quasi-concave and singular normal distribution functions The paper provides a condition for differentiability as well as an equivalent criterion for Lipschitz continuity of singular normal distributions. Such distributions are of interest, for instance, in stochastic optimization ...
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2001-11-13BuchMartingale pricing measures in incomplete markets via stochastic programming duality in the dual of L ∞ We propose a new framework for analyzing pricing theory for incomplete markets and contingent claims, using conjugate duality and optimization theory. Various statements in the literature of the fundamental theorem of asset ...
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2004-04-16BuchMean-risk objectives in stochastic programming Traditional stochastic programming is risk neutral in the sense that it is concerned with the optimization of an expectation criterion. A common approach to addressing risk in decision making problems is to consider a ...
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2000-06-13BuchMean-variance versus expected utility in dynamic investment analysis This paper derives the mean-variance efficient frontier and optimal portfolio policies for a dynamic investment model. In the absence of arbitrage opportunities, the optimal expected portfolio value can be identified through ...
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2012-03-19BuchMeasures of information in multi-stage stochastic programming(Bounds in Multistage Linear Stochastic Programming) Multistage stochastic programs, which involve sequences of decisions over time, areusually hard to solve in realistically sized problems. In the two-stage case, several approaches basedon different levels of available ...
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2004-01-14BuchMelt control This paper introduces melt control as a good case for application of two- and multistage stochastic programming models. Sources of uncertainties are described and several methods of input generation are presented. The ...
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2007-05-29BuchMIP Reformulations of the Probabilistic Set Covering Problem In this paper we address the following probabilistic version (PSC) of the set cover-ing problem: $ min{cx | P(Ax ≥ ξ) ≥ p, x_j \in {0, 1}N }$ where A is a 0-1 matrix, ξ is arandom 0-1 vector and $p \in (0, 1]$ is the ...
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2014-04-16BuchMitigating Uncertainty via Compromise Decisions in Two-stage Stochastic Linear Programming Stochastic Programming (SP) has long been considered as a well-justified yet computationally challenging paradigm for practical applications. Computational studies in the literature often involve approximating a large ...
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2001-06-06BuchModeling farmers' response to uncertain rainfall in Burkina Faso Farmers on the Central Plateau of Burkina Faso in West Africa cultivate under precarious con-ditions. Rainfall variability is extremely high in this area, and accounts for much of the uncertainty surrounding the farmers? ...
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2006-03-20BuchModels for nuclear smuggling interdiction We describe two stochastic network interdiction models for thwarting nuclear smuggling.In the first model, the smuggler travels through a transportation network on a path thatmaximizes the probability of evading detection, ...
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2014-04-04BuchMulti-Objective Probabilistically Constrained Programming with Variable Risk: New Models and Applications We consider a class of multi-objective probabilistically constrained problems MOPCP with a joint chance constraint, a multi-row random technology matrix, and a risk parameter (i.e., the reliability level) defined as a ...
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2011-11-28BuchMultistage Optimization We provide a new identity for the multistage Average Value-at-Risk. The identity is based on the conditional Average Value-at-Risk at random level, which is introduced. It is of interest in situations, where the information ...
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2002-02-21BuchMultistage stochastic convex programs In this paper, we study alternative primal and dual formulations of multistage stochastic convex programs (SP). The alternative dual problems which can be traced to the alterna-tive primal representations, lead to stochastic ...
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2012-03-19BuchMultistage Stochastic Decomposition: A Bridge between Stochastic Programming and Approximate Dynamic Programming Multi-stage stochastic programs (MSP) pose some of the more challenging optimizationproblems. Because such models can become rather intractable in general, it is important todesign algorithms that can provide approximations ...
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2001-04-04BuchMultistage stochastic integer programs We consider linear mulitstage stochastic integer programs and study their functional and dynamic programming formulations as well as conditions for optimality and stability of solutions. Furthermore, we study the application ...
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2005-04-11BuchNotes on free lunch in the limit and pricing by conjugate duality theory King and Korf introduced, in the framework of a discrete-time dynamic market model on a general probability space, a new concept of arbitrage called free lunch in the limit which is slightly weaker than the common free ...
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2008-07-02BuchNumerical Evaluation of Approximation Methods in Stochastic Programming We study an approach for the evaluation of approximation and solution methodsfor multistage linear stochastic programs by measuring the performance of the obtained solutions on a set of out-of-sample scenarios. The main ...