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Introduction to methods for nonlinea...

Grippo, Luigi.

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Introduction to methods for nonlinear optimization

Record Type:	Electronic resources : Monograph/item
Title/Author:	Introduction to methods for nonlinear optimization/ by Luigi Grippo, Marco Sciandrone.
Author:	Grippo, Luigi.
other author:	Sciandrone, Marco.
Published:	Cham :Springer International Publishing : : 2023.,
Description:	1 online resource (xv, 723 p.) :ill., digital ;24 cm.
[NT 15003449]:	1 Introduction -- 2 Fundamental definitions and basic existence results -- 3 Optimality conditions for unconstrained problems in Rn -- 4 Optimality conditions for problems with convex feasible set -- 5 Optimality conditions for Nonlinear Programming -- 6 Duality theory -- 7 Optimality conditions based on theorems of the alternative -- 8 Basic concepts on optimization algorithms -- 9 Unconstrained optimization algorithms -- 10 Line search methods -- 11 Gradient method -- 12 Conjugate direction methods -- 13 Newton's method -- 14 Trust region methods -- 15 Quasi-Newton Methods -- 16 Methods for nonlinear equations -- 17 Methods for least squares problems -- 18 Methods for large-scale optimization -- 19 Derivative-free methods for unconstrained optimization -- 20 Methods for problems with convex feasible set -- 21 Penalty and augmented Lagrangian methods -- 22 SQP methods -- 23 Introduction to interior point methods -- 24 Nonmonotone methods -- 25 Spectral gradient methods -- 26 Decomposition methods -- Appendix A: basic concepts of linear algebra and analysis -- Appendix B: Differentiation in Rn -- Appendix C: Introduction to convex analysis.
Contained By:	Springer Nature eBook
Subject:	Mathematical optimization - Methodology. -
Online resource:	https://doi.org/10.1007/978-3-031-26790-1
ISBN:	9783031267901

Introduction to methods for nonlinear optimization
Grippo, Luigi.

Introduction to methods for nonlinear optimization[electronic resource] /by Luigi Grippo, Marco Sciandrone. - Cham :Springer International Publishing :2023. - 1 online resource (xv, 723 p.) :ill., digital ;24 cm. - La matematica per il 3+2,v. 1522038-5757 ;. - La matematica per il 3+2 ;v. 152..

1 Introduction -- 2 Fundamental definitions and basic existence results -- 3 Optimality conditions for unconstrained problems in Rn -- 4 Optimality conditions for problems with convex feasible set -- 5 Optimality conditions for Nonlinear Programming -- 6 Duality theory -- 7 Optimality conditions based on theorems of the alternative -- 8 Basic concepts on optimization algorithms -- 9 Unconstrained optimization algorithms -- 10 Line search methods -- 11 Gradient method -- 12 Conjugate direction methods -- 13 Newton's method -- 14 Trust region methods -- 15 Quasi-Newton Methods -- 16 Methods for nonlinear equations -- 17 Methods for least squares problems -- 18 Methods for large-scale optimization -- 19 Derivative-free methods for unconstrained optimization -- 20 Methods for problems with convex feasible set -- 21 Penalty and augmented Lagrangian methods -- 22 SQP methods -- 23 Introduction to interior point methods -- 24 Nonmonotone methods -- 25 Spectral gradient methods -- 26 Decomposition methods -- Appendix A: basic concepts of linear algebra and analysis -- Appendix B: Differentiation in Rn -- Appendix C: Introduction to convex analysis.

ISBN: 9783031267901

Standard No.: 10.1007/978-3-031-26790-1doiSubjects--Topical Terms:

3634524
Mathematical optimization
--Methodology.

LC Class. No.: QA427

Dewey Class. No.: 519.6

Introduction to methods for nonlinear optimization
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505 0 $a 1 Introduction -- 2 Fundamental definitions and basic existence results -- 3 Optimality conditions for unconstrained problems in Rn -- 4 Optimality conditions for problems with convex feasible set -- 5 Optimality conditions for Nonlinear Programming -- 6 Duality theory -- 7 Optimality conditions based on theorems of the alternative -- 8 Basic concepts on optimization algorithms -- 9 Unconstrained optimization algorithms -- 10 Line search methods -- 11 Gradient method -- 12 Conjugate direction methods -- 13 Newton's method -- 14 Trust region methods -- 15 Quasi-Newton Methods -- 16 Methods for nonlinear equations -- 17 Methods for least squares problems -- 18 Methods for large-scale optimization -- 19 Derivative-free methods for unconstrained optimization -- 20 Methods for problems with convex feasible set -- 21 Penalty and augmented Lagrangian methods -- 22 SQP methods -- 23 Introduction to interior point methods -- 24 Nonmonotone methods -- 25 Spectral gradient methods -- 26 Decomposition methods -- Appendix A: basic concepts of linear algebra and analysis -- Appendix B: Differentiation in Rn -- Appendix C: Introduction to convex analysis.
650 0 $a Mathematical optimization $x Methodology. $3 3634524
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