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Books > Science & Mathematics > Mathematics > Calculus & mathematical analysis > Calculus of variations
Simulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning introduces the evolving area of simulation-based optimization. Since it became possible to analyze random systems using computers, scientists and engineers have sought the means to optimize systems using simulation models. Only recently, however, has this objective had success in practice. Cutting-edge work in computational operations research, including non-linear programming (simultaneous perturbation), dynamic programming (reinforcement learning), and game theory (learning automata) has made it possible to use simulation in conjunction with optimization techniques. As a result, this research has given simulation added dimensions and power that it did not have in the recent past. 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.
This manual contains worked-out solutions for all odd-numbered exercises in Larson/Edwards' CALCULUS OF A SINGLE VARIABLE: EARLY TRANSCENDENTAL FUNCTIONS, 7th Edition (Chapters 1-10 of CALCULUS: EARLY TRANSCENDENTAL FUNCTIONS, 7th Edition). |
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