Stochastic Optimization Methods
Stochastic Optimization Methods This book examines optimization problems that in practice involve random model parameters. Based on the probability distribution of the random data and using decision…
Specifikacia Stochastic Optimization Methods
Stochastic Optimization Methods
This book examines optimization problems that in practice involve random model parameters. Based on the probability distribution of the random data and using decision theoretical concepts, optimization problems under stochastic uncertainty are converted into appropriate deterministic substitute problems.§§Due to the probabilities and expectations involved, the book also shows how to apply approximative solution techniques. It details the computation of robust optimal solutions, i.e., optimal solutions that are insensitive with respect to random parameter variations, where appropriate deterministic substitute problems are needed.
In particular, it now shows how to apply stochastic optimization methods to the approximate solution of important concrete problems arising in engineering, economics and operations research.§ Several deterministic and stochastic approximation methods are provided: Taylor expansion methods, regression and response surface methods (RSM), probability inequalities, multiple linearization of survival/failure domains, discretization methods, convex approximation/deterministic descent directions/efficient points, stochastic approximation and gradient procedures and differentiation formulas for probabilities and expectations.§§In the third edition, this book further develops stochastic optimization methods.
Stochastic Optimization Methods
Basic Stochastic Processes
Practical Optimization
Nonlinear Optimization
Inventory Optimization