Browsing Stochastic Programming Eprint Series (SPEPS) by Title
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20090522BuchFenchel Decomposition for Stochastic MixedIntegerProgramming This paper introduces a new cutting plane method for twostage stochastic mixedinteger programming (SMIP) called Fenchel decomposition (FD). FD usesa class of valid inequalities termed, FD cuts, which are derived based ...

20000216BuchFinite capacity production planning with random demand and limited information Production planning has a fundamental role in any manufacturing operation. The problem is to decide what type of, and how much, product should be produced in future time periods. The decisions should be based on many ...

20021024BuchFrontiers of stochastically nondominated portfolios We consider the problem of constructing a portfolio of finitely many assets whose returns are described by a discrete joint distribution. We propose meanrisk models which are solvable by linear programming and generate ...

20060320BuchGenetic algorithm based technique for solving chance constrained problems Management and measurement of risk is an important issue in almost all areas that require decisions to be made under uncertain information. Chance Constrained Programming (CCP) have been used for modelling and analysis of ...

20120220BuchGradient estimates for Gaussian distribution functions: Application to probabilistically constrained optimization problems We provide lower estimates for the norm of gradients of Gaussian distribution functions and apply the results obtained to a special class ofprobabilistically constrained optimization problems. In particular, it is shown ...

19991220BuchHedging electricity portfolios via stochastic programming Electricity producers participating in the Nordic wholesalelevel market face significant uncertainty in inflow to reservoirs and prices in the spot and contract markets. Taking the view of a single riskaverse producer, ...

20020503BuchHigherOrder Upper Bounds on the Expectation of a Convex Function We develop a decreasing sequence of upper bounds on the expectation of a convex function. The nth term in the sequence uses moments and crossmoments of up to degree n from the underlying random vector. Our work has ...

20030306BuchHölder and Lipschitz Stability of Solution Sets in Programs with Probabilistic Constraints We study perturbations of a stochastic program with a probabilistic constraint and $r$concave original probability distribution. First we improve our earlier results substantially and provide conditions implying Hölder ...

20020729BuchIntegrated chance constraints reduced forms and an algorithmWe consider integrated chance constraints (ICC), which provide quantitative alternatives for traditional chance constraints. We derive explicit polyhedral descriptions for the convex feasible sets induced by ICCs, for the ...

20030704BuchIntegrated chance constraints in an ALM model for pension funds We discuss integrated chance constraints in their role of shortterm risk constraints in a strategic ALM model for Dutch pension funds. The problem is set up as a multistage recourse model, with special attention for ...

20020604BuchIntegration quadratures in discretization of stochastic programs Because of its simplicity, conditional sampling is the most popular method for generating scenario trees in stochastic programming. It is based on approximating probability measures by empirical ones generated by random ...

20030930BuchIntertemporal meanvariance efficiency with a Markovian state price density This paper extends Merton's continuous time (instantaneous) meanvariance analysis and the mutual fund separation theory. Given the existence of a Markovian state price density process, the optimal portfolios from concave ...

20000120BuchIntertemporal Surplus Management This paper presents an intertemporal portfolio selection model for pension funds that maximize the intertemporal expected utility of the surplus of assets net of liabilities. Following Merton (1973) it is assumed that both ...

20120608BuchIntroduction to convex optimization in financial markets Convexity arises quite naturally in financial risk management. In riskpreferences concerning random cashflows, convexity corresponds to thefundamental diversification principle. Convexity is a basic property alsoof budget ...

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 ...