Simulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning introduces the evolving area of simulation-based optimization. The book's objective is two-fold: (1) It examines the mathematical governing principles of simulation-based optimization, thereby providing the reader with the ability to model relevant real-life problems using these techniques. (2) It outlines the computational technology underlying these methods. Taken together these two aspects demonstrate that the mathematical and computational methods discussed in this book do work. Broadly speaking, the book has two parts: (1) parametric (static) optimization and (2) control (dynamic) optimization. Some of the book's special features are: *An accessible introduction to reinforcement learning and parametric-optimization techniques. *A step-by-step description of several algorithms of simulation-based optimization. *A clear and simple introduction to the methodology of neural networks. *A gentle introduction to convergence analysis of some of the methods enumerated above. *Computer programs for many algorithms of simulation-based optimization.So if the linear system is defined as: -agt; AAm = b, you can solve for the vector Ap in MATLAB by writing the following one line! x=A\b; This ... The simulator and the optimization algorithm (such as simulated annealing, tabu search or the genetic algorithm) can be written in the same program. ... These toolboxes contain codes anbsp;...

Title | : | Simulation-Based Optimization |

Author | : | Abhijit Gosavi |

Publisher | : | Springer Science & Business Media - 2013-03-14 |

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