國立高雄大學圖資館 |

Language: English

Back

Methods of nonsmooth optimization in stochastic programmingfrom conceptual algorithms to real-world applications /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Methods of nonsmooth optimization in stochastic programmingby Wim Stefanus van Ackooij, Welington Luis de Oliveira.
Reminder of title:	from conceptual algorithms to real-world applications /
Author:	Ackooij, Wim Stefanus van.
other author:	De Oliveira, Welington Luis.
Published:	Cham :Springer Nature Switzerland :2025.
Description:	xvi, 570 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Stochastic programming.
Online resource:	https://doi.org/10.1007/978-3-031-84837-7
ISBN:	9783031848377$q(electronic bk.)

Methods of nonsmooth optimization in stochastic programmingfrom conceptual algorithms to real-world applications /
Ackooij, Wim Stefanus van.

Methods of nonsmooth optimization in stochastic programmingfrom conceptual algorithms to real-world applications /[electronic resource] :by Wim Stefanus van Ackooij, Welington Luis de Oliveira. - Cham :Springer Nature Switzerland :2025. - xvi, 570 p. :ill. (some col.), digital ;24 cm. - International series in operations research & management science,v. 3632214-7934 ;. - International series in operations research & management science ;v. 140..

Introduction -- Primer of convex analysis -- Variational analysis -- Linear and nonlinear optimization problems -- Probability and Statistics -- Fundamental modeling questions in stochastic programming -- Adjusting to uncertainty: modeling recourse -- Probability constraints -- Proximal point algorithms for problems with structure -- Cutting-plane algorithms for nonsmooth convex optimization over simple domains -- Bundle methods for nonsmooth convex optimization over simple domains -- Methods for nonlinearly constrained nonsmooth optimization problems -- Methods for nonsmooth optimization with mixed-integer variables -- Methods for nonsmooth nonconvex optimization -- Two-stage stochastic programs -- Progressive decoupling in multistage stochastic programming -- Scenario decomposition with alternating projections -- Methods for multistage stochastic linear programs -- Methods for handling probability.

This book presents a comprehensive series of methods in nonsmooth optimization, with a particular focus on their application in stochastic programming and dedicated algorithms for decision-making under uncertainty. Each method is accompanied by rigorous mathematical analysis, ensuring a deep understanding of the underlying principles. The theoretical discussions included are essential for comprehending the mechanics of various algorithms and the nature of the solutions they provide-whether they are global, local, stationary, or critical. The book begins by introducing fundamental tools from set-valued analysis, optimization, and probability theory. It then transitions from deterministic to stochastic optimization, starting with a thorough discussion of modeling, understanding uncertainty, and incorporating it into optimization problems. Following this foundation, the book explores numerical algorithms for nonsmooth optimization, covering well-known decomposition techniques and algorithms for convex optimization, mixed-integer convex programming, and nonconvex optimization. Additionally, it introduces numerical algorithms specifically for stochastic programming, focusing on stochastic programming with recourse, chance-constrained optimization, and detailed algorithms for both risk-neutral and risk-averse multistage stochastic programs. The book guides readers through the entire process, from defining optimization models for practical problems to presenting implementable algorithms that can be applied in practice. It is intended for students, practitioners, and scholars who may be unfamiliar with stochastic programming and nonsmooth optimization. The analyses provided are also valuable for practitioners who may not be interested in convergence proofs but wish to understand the nature of the solutions obtained.

ISBN: 9783031848377$q(electronic bk.)

Standard No.: 10.1007/978-3-031-84837-7doiSubjects--Topical Terms:

186295
Stochastic programming.

LC Class. No.: T57.79

Dewey Class. No.: 519.7

Methods of nonsmooth optimization in stochastic programmingfrom conceptual algorithms to real-world applications /
LDR:03991nmm a2200361 a 4500 001 686975
003 DE-He213
005 20250506125928.0
006 m d
007 cr nn 008maaau
008 260309s2025 sz s 0 eng d
020 $a 9783031848377$q(electronic bk.)
020 $a 9783031848360$q(paper)
024 7 $a 10.1007/978-3-031-84837-7 $2 doi
035 $a 978-3-031-84837-7
040 $a GP $c GP
041 0 $a eng
050 4 $a T57.79
072 7 $a KJT $2 bicssc
072 7 $a KJMD $2 bicssc
072 7 $a BUS049000 $2 bisacsh
072 7 $a KJT $2 thema
072 7 $a KJMD $2 thema
082 0 4 $a 519.7 $2 23
090 $a T57.79 $b .A182 2025
100 1 $a Ackooij, Wim Stefanus van. $3 1001444
245 1 0 $a Methods of nonsmooth optimization in stochastic programming $h [electronic resource] : $b from conceptual algorithms to real-world applications / $c by Wim Stefanus van Ackooij, Welington Luis de Oliveira.
260 $a Cham : $b Springer Nature Switzerland : $b Imprint: Springer, $c 2025.
300 $a xvi, 570 p. : $b ill. (some col.), digital ; $c 24 cm.
490 1 $a International series in operations research & management science, $x 2214-7934 ; $v v. 363
505 0 $a Introduction -- Primer of convex analysis -- Variational analysis -- Linear and nonlinear optimization problems -- Probability and Statistics -- Fundamental modeling questions in stochastic programming -- Adjusting to uncertainty: modeling recourse -- Probability constraints -- Proximal point algorithms for problems with structure -- Cutting-plane algorithms for nonsmooth convex optimization over simple domains -- Bundle methods for nonsmooth convex optimization over simple domains -- Methods for nonlinearly constrained nonsmooth optimization problems -- Methods for nonsmooth optimization with mixed-integer variables -- Methods for nonsmooth nonconvex optimization -- Two-stage stochastic programs -- Progressive decoupling in multistage stochastic programming -- Scenario decomposition with alternating projections -- Methods for multistage stochastic linear programs -- Methods for handling probability.
520 $a This book presents a comprehensive series of methods in nonsmooth optimization, with a particular focus on their application in stochastic programming and dedicated algorithms for decision-making under uncertainty. Each method is accompanied by rigorous mathematical analysis, ensuring a deep understanding of the underlying principles. The theoretical discussions included are essential for comprehending the mechanics of various algorithms and the nature of the solutions they provide-whether they are global, local, stationary, or critical. The book begins by introducing fundamental tools from set-valued analysis, optimization, and probability theory. It then transitions from deterministic to stochastic optimization, starting with a thorough discussion of modeling, understanding uncertainty, and incorporating it into optimization problems. Following this foundation, the book explores numerical algorithms for nonsmooth optimization, covering well-known decomposition techniques and algorithms for convex optimization, mixed-integer convex programming, and nonconvex optimization. Additionally, it introduces numerical algorithms specifically for stochastic programming, focusing on stochastic programming with recourse, chance-constrained optimization, and detailed algorithms for both risk-neutral and risk-averse multistage stochastic programs. The book guides readers through the entire process, from defining optimization models for practical problems to presenting implementable algorithms that can be applied in practice. It is intended for students, practitioners, and scholars who may be unfamiliar with stochastic programming and nonsmooth optimization. The analyses provided are also valuable for practitioners who may not be interested in convergence proofs but wish to understand the nature of the solutions obtained.
650 0 $a Stochastic programming. $3 186295
650 0 $a Nonsmooth optimization. $3 229311
650 0 $a Calculus. $3 183091
650 1 4 $a Operations Research and Decision Theory. $3 914898
650 2 4 $a Optimization. $3 274084
650 2 4 $a Operations Research, Management Science. $3 511451
650 2 4 $a Numerical Analysis. $3 275681
650 2 4 $a Continuous Optimization. $3 558846
650 2 4 $a Stochastic Calculus. $3 922618
700 1 $a De Oliveira, Welington Luis. $3 1001445
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer Nature eBook
830 0 $a International series in operations research & management science ; $v v. 140. $3 490892
856 4 0 $u https://doi.org/10.1007/978-3-031-84837-7
950 $a Business and Management (SpringerNature-41169)