Browsing Stochastic Programming Eprint Series (SPEPS) by Title
Now showing items 122141 of 240

20021105BuchLearning 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 ...

20170731BuchLearning 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 ...

20050426BuchLipschitz and differentiability properties of quasiconcave 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 ...

20011113BuchMartingale 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 ...

20040416BuchMeanrisk 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 ...

20000613BuchMeanvariance versus expected utility in dynamic investment analysis This paper derives the meanvariance 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 ...

20120319BuchMeasures of information in multistage 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 twostage case, several approaches basedon different levels of available ...

20040114BuchMelt control Charge optimization via stochastic programmingThis 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 ...

20070529BuchMIP Reformulations of the Probabilistic Set Covering Problem In this paper we address the following probabilistic version (PSC) of the set covering problem: $ min{cx  P(Ax ≥ ξ) ≥ p, x_j \in {0, 1}N }$ where A is a 01 matrix, ξ is arandom 01 vector and $p \in (0, 1]$ is the ...

20140416BuchMitigating Uncertainty via Compromise Decisions in Twostage Stochastic Linear Programming Stochastic Programming (SP) has long been considered as a welljustified yet computationally challenging paradigm for practical applications. Computational studies in the literature often involve approximating a large ...

20010606BuchModeling farmers' response to uncertain rainfall in Burkina Faso a stochastic programming approachFarmers on the Central Plateau of Burkina Faso in West Africa cultivate under precarious conditions. Rainfall variability is extremely high in this area, and accounts for much of the uncertainty surrounding the farmers? ...

20060320BuchModels 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, ...

20140404BuchMultiObjective Probabilistically Constrained Programming with Variable Risk: New Models and Applications We consider a class of multiobjective probabilistically constrained problems MOPCP with a joint chance constraint, a multirow random technology matrix, and a risk parameter (i.e., the reliability level) defined as a ...

20111128BuchMultistage Optimization We provide a new identity for the multistage Average ValueatRisk. The identity is based on the conditional Average ValueatRisk at random level, which is introduced. It is of interest in situations, where the information ...

20020221BuchMultistage stochastic convex programs Duality and its implicationsIn 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 alternative primal representations, lead to stochastic ...

20120319BuchMultistage Stochastic Decomposition: A Bridge between Stochastic Programming and Approximate Dynamic Programming Multistage 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 ...

20010404BuchMultistage stochastic integer programs An introductionWe 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 ...

20050411BuchNotes on free lunch in the limit and pricing by conjugate duality theory King and Korf introduced, in the framework of a discretetime 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 ...

20080702BuchNumerical 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 outofsample scenarios. The main ...

20040913BuchOn deviation measures in stochastic integer programming We propose extensions of traditional expectationbased stochastic integer programs to meanrisk models. Risk is measured by expected deviations of suitable random variables from their means or from preselected targets. We ...