Stochastic Optimization Methods

Stochastic Optimization Methods

Dr. Kurt Marti (auth.)
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Optimization problems arising in practice involve random model parameters. For the computation of robust optimal solutions, i.e., optimal solutions being insensitive with respect to random parameter variations, appropriate deterministic substitute problems are needed. 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 occurring probabilities and expectations, approximative solution techniques must be applied. 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, differentiation formulas for probabilities and expectations.

ক্যাটাগোরিগুলো:
সাল:
2008
সংস্করণ:
2nd ed.
প্রকাশক:
Springer Berlin Heidelberg
ভাষা:
english
পৃষ্ঠা:
335
ISBN 10:
3540794581
ISBN 13:
9783540794585
ফাইল:
PDF, 5.09 MB
IPFS:
CID , CID Blake2b
english, 2008
কপিরাইট ধারকের অভিযোগের কারণে এই বইটির ডাউনলোড অনুপলব্ধ

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Pravin Lal

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