國立虎尾科技大學 |

Optimization Under Stochastic Uncertainty = Methods, Control and Random Search Methods /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Optimization Under Stochastic Uncertainty/ by Kurt Marti.
其他題名:	Methods, Control and Random Search Methods /
作者:	Marti, Kurt.
面頁冊數:	XIV, 393 p. 9 illus.online resource. :
Contained By:	Springer Nature eBook
標題:	Computer Science, general. -
電子資源:	https://doi.org/10.1007/978-3-030-55662-4
ISBN:	9783030556624

Optimization Under Stochastic Uncertainty = Methods, Control and Random Search Methods /
Marti, Kurt.

Optimization Under Stochastic UncertaintyMethods, Control and Random Search Methods /[electronic resource] :by Kurt Marti. - 1st ed. 2020. - XIV, 393 p. 9 illus.online resource. - International Series in Operations Research & Management Science,2960884-8289 ;. - International Series in Operations Research & Management Science,227.

1. Optimal Control under Stochastic Uncertainty -- 2. Stochastic Optimization of Regulators -- 3. Optimal Open-Loop Control of Dynamic Systems under Stochastic Uncertainty -- 4. Construction of feedback control by means of homotopy methods -- 5. Constructions of Limit State Functions -- 6. Random Search Procedures for Global Optimization -- 7. Controlled Random Search under Uncertainty -- 8. Controlled Random Search Procedures for Global Optimization -- 9. Mathematical Model of Random Search Methods and Elementary Properties -- 10. Special Random Search Methods -- 11. Accessibility Theorems -- 12. Convergence Theorems -- 13. Convergence of Stationary Random Search Methods for Positive Success Probability -- 14. Random Search Methods of convergence order U(n−") -- 15. Random Search Methods with a Linear Rate of Convergence -- 16. Success/Failure-driven Random Direction Procedures -- 17. Hybrid Methods -- 18. Solving optimization problems under stochastic uncertainty by Random Search Methods(RSM).

This book examines application and methods to incorporating stochastic parameter variations into the optimization process to decrease expense in corrective measures. Basic types of deterministic substitute problems occurring mostly in practice involve i) minimization of the expected primary costs subject to expected recourse cost constraints (reliability constraints) and remaining deterministic constraints, e.g. box constraints, as well as ii) minimization of the expected total costs (costs of construction, design, recourse costs, etc.) subject to the remaining deterministic constraints. After an introduction into the theory of dynamic control systems with random parameters, the major control laws are described, as open-loop control, closed-loop, feedback control and open-loop feedback control, used for iterative construction of feedback controls. For approximate solution of optimization and control problems with random parameters and involving expected cost/loss-type objective, constraint functions, Taylor expansion procedures, and Homotopy methods are considered, Examples and applications to stochastic optimization of regulators are given. Moreover, for reliability-based analysis and optimal design problems, corresponding optimization-based limit state functions are constructed. Because of the complexity of concrete optimization/control problems and their lack of the mathematical regularity as required of Mathematical Programming (MP) techniques, other optimization techniques, like random search methods (RSM) became increasingly important. Basic results on the convergence and convergence rates of random search methods are presented. Moreover, for the improvement of the – sometimes very low – convergence rate of RSM, search methods based on optimal stochastic decision processes are presented. In order to improve the convergence behavior of RSM, the random search procedure is embedded into a stochastic decision process for an optimal control of the probability distributions of the search variates (mutation random variables). .

ISBN: 9783030556624

Standard No.: 10.1007/978-3-030-55662-4doiSubjects--Topical Terms:

669807
Computer Science, general.

LC Class. No.: HD30.23

Dewey Class. No.: 658.40301

Optimization Under Stochastic Uncertainty = Methods, Control and Random Search Methods /
LDR:04571nam a22004215i 4500 001 1025369
003 DE-He213
005 20201110153123.0
007 cr nn 008mamaa
008 210318s2020 gw | s |||| 0|eng d
020 $a 9783030556624 $9 978-3-030-55662-4
024 7 $a 10.1007/978-3-030-55662-4 $2 doi
035 $a 978-3-030-55662-4
050 4 $a HD30.23
072 7 $a KJT $2 bicssc
072 7 $a BUS049000 $2 bisacsh
072 7 $a KJT $2 thema
072 7 $a KJMD $2 thema
082 0 4 $a 658.40301 $2 23
100 1 $a Marti, Kurt. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 669527
245 1 0 $a Optimization Under Stochastic Uncertainty $h [electronic resource] : $b Methods, Control and Random Search Methods / $c by Kurt Marti.
250 $a 1st ed. 2020.
264 1 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2020.
300 $a XIV, 393 p. 9 illus. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
490 1 $a International Series in Operations Research & Management Science, $x 0884-8289 ; $v 296
505 0 $a 1. Optimal Control under Stochastic Uncertainty -- 2. Stochastic Optimization of Regulators -- 3. Optimal Open-Loop Control of Dynamic Systems under Stochastic Uncertainty -- 4. Construction of feedback control by means of homotopy methods -- 5. Constructions of Limit State Functions -- 6. Random Search Procedures for Global Optimization -- 7. Controlled Random Search under Uncertainty -- 8. Controlled Random Search Procedures for Global Optimization -- 9. Mathematical Model of Random Search Methods and Elementary Properties -- 10. Special Random Search Methods -- 11. Accessibility Theorems -- 12. Convergence Theorems -- 13. Convergence of Stationary Random Search Methods for Positive Success Probability -- 14. Random Search Methods of convergence order U(n−") -- 15. Random Search Methods with a Linear Rate of Convergence -- 16. Success/Failure-driven Random Direction Procedures -- 17. Hybrid Methods -- 18. Solving optimization problems under stochastic uncertainty by Random Search Methods(RSM).
520 $a This book examines application and methods to incorporating stochastic parameter variations into the optimization process to decrease expense in corrective measures. Basic types of deterministic substitute problems occurring mostly in practice involve i) minimization of the expected primary costs subject to expected recourse cost constraints (reliability constraints) and remaining deterministic constraints, e.g. box constraints, as well as ii) minimization of the expected total costs (costs of construction, design, recourse costs, etc.) subject to the remaining deterministic constraints. After an introduction into the theory of dynamic control systems with random parameters, the major control laws are described, as open-loop control, closed-loop, feedback control and open-loop feedback control, used for iterative construction of feedback controls. For approximate solution of optimization and control problems with random parameters and involving expected cost/loss-type objective, constraint functions, Taylor expansion procedures, and Homotopy methods are considered, Examples and applications to stochastic optimization of regulators are given. Moreover, for reliability-based analysis and optimal design problems, corresponding optimization-based limit state functions are constructed. Because of the complexity of concrete optimization/control problems and their lack of the mathematical regularity as required of Mathematical Programming (MP) techniques, other optimization techniques, like random search methods (RSM) became increasingly important. Basic results on the convergence and convergence rates of random search methods are presented. Moreover, for the improvement of the – sometimes very low – convergence rate of RSM, search methods based on optimal stochastic decision processes are presented. In order to improve the convergence behavior of RSM, the random search procedure is embedded into a stochastic decision process for an optimal control of the probability distributions of the search variates (mutation random variables). .
650 2 4 $a Computer Science, general. $3 669807
650 2 4 $a Probability Theory and Stochastic Processes. $3 593945
650 1 4 $a Operations Research/Decision Theory. $3 669176
650 0 $a Computer science. $3 573171
650 0 $a Probabilities. $3 527847
650 0 $a Decision making. $3 528319
650 0 $a Operations research. $3 573517
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9783030556617
776 0 8 $i Printed edition: $z 9783030556631
776 0 8 $i Printed edition: $z 9783030556648
830 0 $a International Series in Operations Research & Management Science, $x 0884-8289 ; $v 227 $3 1254441
856 4 0 $u https://doi.org/10.1007/978-3-030-55662-4
912 $a ZDB-2-BUM
912 $a ZDB-2-SXBM
950 $a Business and Management (SpringerNature-41169)
950 $a Business and Management (R0) (SpringerNature-43719)