Algorithms and Applications
Band: 54, 435 Seiten, Gebunden, HC runder Rücken kaschiert, Format (B × H): 160 mm x 241 mm, Gewicht: 1780 g
Reihe: Applied Optimization
Verlag: Springer US
Pardalos / Uryasev Stochastic OptimizationStochastic 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. Recently, the practical experience gained in stochastic programming has been expanded to a much larger spectrum of applications including financial modeling, 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.
Weitere Infos & Material
Preface. Output analysis for approximated stochastic programs; J. Dupacová. 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.