Uryasev

Stochastic Optimization: Algorithms and Applications - Applied Optimization Uryasev 2001 edition

Marc Notes: Includes bibliographical references and index. Table of Contents: Preface. Output analysis for approximated stochastic programs; J. Dupacova. Combinatorial Randomized Rounding: Boosting Randomized Rounding with Combinatorial Arguments; P. Efraimidis, P. G. Spirakis. Statutory Regulation of Casualty Insurance Companies: An Example from Norway with Stochastic Programming Analysis; A. Gaivoronski, et al. Option pricing in a world with arbitrage; X. Guo, L. Shepp. Monte Carlo Methods for Discrete Stochastic Optimization; T. Homem-de-Mello. Discrete Approximation in Quantile Problem of Portfolio Selection; A. Kibzun, R. Lepp. Optimizing electricity distribution using two-stage integer recourse models; W. K. Klein Haneveld, M. H. van der Vlerk. A Finite-Dimensional Approach to Infinite-Dimensional Constraints in Stochastic Programming Duality; L. Korf. Non-Linear Risk of Linear Instruments; A. Kreinin. Multialgorithms for Parallel Computing: A New Paradigm for Optimization; J. Nazareth. Convergence Rate of Incremental Subgradient Algorithms; A. Nedic, D. Bertsekas. Transient Stochastic Models for Search Patterns; E. Pasiliao. Value-at-Risk Based Portfolio Optimization; A. Puelz. Combinatorial Optimization, Cross-Entropy, Ants and Rare Events; R. Y. Rubinstein. Consistency of Statistical Estimators: the Epigraphical View; G. Salinetti. Hierarchical Sparsity in Multistage Convex Stochastic Programs; M. Steinbach. Conditional Value-at-Risk: Optimization Approach; S. Uryasev, R. T. Rockafellar."Publisher Marketing: Stochastic programming is the study of procedures for decision making under the presence of uncertainties and risks. Stochastic programming approaches have been successfully used in a number of areas such as energy and production planning, telecommunications, and transportation. The practical experience gained in stochastic programming has been expanded to a much larger spectrum of applications including financial modelling, risk management, and probabilistic risk analysis. Major topics in this volume include: (1) advances in theory and implementation of stochastic programming algorithms; (2) sensitivity analysis of stochastic systems; (3) stochastic programming applications and other related topics. Audience: Researchers and academies working in optimization, computer modeling, operations research and financial engineering. The book is appropriate as supplementary reading in courses on optimization and financial engineering.

Contributor Bio:  Pardalos, Panos M Panos M. Pardalos is one of the leading experts in global optimization and control theory. V. Yatsenko's research is connected with control of bilinear systems, nonlinear estimation, control of quantum systems, and globabl optimization problems. Both Pardalos and Yatsenko have authored numerous publications including books and well-known scientific journals.