Probabilistic Constrained Optimization

Methodology and Applications

Inbunden, Engelska, 2000

1 379 kr

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Probabilistic and percentile/quantile functions play an important role in several applications, such as finance (Value-at-Risk), nuclear safety, and the environment. Recently, significant advances have been made in sensitivity analysis and optimization of probabilistic functions, which is the basis for construction of new efficient approaches. This work presents the state-of-the-art in the theory of optimization of probabilistic functions and several engineering and finance applications, including material flow systems, production planning, Value-at-Risk, asset and liability management, and optimal trading strategies for financial derivatives (options).

Produktinformation

Utgivningsdatum2000-11-30
Mått155 x 235 x 23 mm
Vikt653 g
FormatInbunden
SpråkEngelska
SerieNonconvex Optimization and Its Applications
Antal sidor308
Upplaga2001
FörlagKluwer Academic Publishers
ISBN9780792366447

Tillhör följande kategorier

to the Theory of Probabilistic Functions and Percentiles.- Pricing American Options by Simulation Using a Stochastic Mesh with Optimized Weights.- On Optimization of Unreliable Material Flow Systems.- Stochastic Optimization in Asset & Liability Management: A Model for Non-Maturing Accounts.- Optimization in the Space of Distribution Functions and Applications in the Bayes Analysis.- Sensitivity Analysis of Worst-Case Distribution for Probability Optimization Problems.- On Maximum Reliability Problem in Parallel-Series Systems with Two Failure Modes.- Robust Monte Carlo Simulation for Approximate Covariance Matrices and VaR Analyses.- Structure of Optimal Stopping Strategies for American Type Options.- Approximation of Value-at-Risk Problems with Decision Rules.- Managing Risk with Expected Shortfall.- On the Numerical Solution of Jointly Chance Constrained Problems.- Management of Quality of Service through Chance-constraints in Multimedia Networks.- Solution of a Product Substitution Problem Using Stochastic Programming.- Some Remarks on the Value-at-Risk and the Conditional Value-at-Risk.- Statistical Inference of Stochastic Optimization Problems.