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Smooth Nonlinear Optimization of Rn - Rapcsák, Tamás
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Rapcsák, Tamás:
Smooth Nonlinear Optimization of Rn - gebunden oder broschiert

1997, ISBN: 0792346807, Lieferbar binnen 4-6 Wochen Versandkosten:Versandkostenfrei innerhalb der BRD

ID: 9780792346807

Internationaler Buchtitel. In englischer Sprache. Verlag: Springer-Verlag GmbH, HC runder Rücken kaschiert, 396 Seiten, L=235mm, B=155mm, H=26mm, Gew.=742gr, [GR: 16280 - HC/Mathematik/Wahrscheinlichkeitstheorie], [SW: - Differenzialgeometrie], Gebunden, Klappentext: This book is the first uniform, differential geometric approach to smooth nonlinear optimization. This advance allows the author to improve the sufficiency part of the Lagrange multiplier rule introduced in 1788 and to solve Fenchel's problem of level sets (1953) in the smooth case. Furthermore, this permits the author to replace convexity by geodesic convexity and apply it in complementarity systems, to study the nonlinear coordinate representations of smooth optimization problems, to describe the structure by tensors, to introduce a general framework for variable metric methods containing many basic nonlinear optimization algorithms, and - last but not least - to generate a class of polynomial interior point algorithms for linear optimization by a subclass of Riemannian metrics. Audience: The book is addressed to graduate students and researchers. The elementary notions necessary for understanding the material constitute part of the standard university curriculum. This book is the first uniform, differential geometric approach to smooth nonlinear optimization. This advance allows the author to improve the sufficiency part of the Lagrange multiplier rule introduced in 1788 and to solve Fenchel's problem of level sets (1953) in the smooth case. Furthermore, this permits the author to replace convexity by geodesic convexity and apply it in complementarity systems, to study the nonlinear coordinate representations of smooth optimization problems, to describe the structure by tensors, to introduce a general framework for variable metric methods containing many basic nonlinear optimization algorithms, and - last but not least - to generate a class of polynomial interior point algorithms for linear optimization by a subclass of Riemannian metrics. Audience: The book is addressed to graduate students and researchers. The elementary notions necessary for understanding the material constitute part of the standard university curriculum.

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Smooth Nonlinear Optimization of Rn - Rapcsák, Tamás
Vergriffenes Buch, derzeit bei uns nicht verfügbar.
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Rapcsák, Tamás:
Smooth Nonlinear Optimization of Rn - gebunden oder broschiert

1997, ISBN: 0792346807, Lieferbar binnen 4-6 Wochen

ID: 9780792346807

Internationaler Buchtitel. In englischer Sprache. Verlag: Springer-Verlag GmbH, HC runder Rücken kaschiert, 396 Seiten, L=235mm, B=155mm, H=26mm, Gew.=742gr, [GR: 16280 - HC/Mathematik/Wahrscheinlichkeitstheorie], [SW: - Differenzialgeometrie], Gebunden, Klappentext: This book is the first uniform, differential geometric approach to smooth nonlinear optimization. This advance allows the author to improve the sufficiency part of the Lagrange multiplier rule introduced in 1788 and to solve Fenchel's problem of level sets (1953) in the smooth case. Furthermore, this permits the author to replace convexity by geodesic convexity and apply it in complementarity systems, to study the nonlinear coordinate representations of smooth optimization problems, to describe the structure by tensors, to introduce a general framework for variable metric methods containing many basic nonlinear optimization algorithms, and - last but not least - to generate a class of polynomial interior point algorithms for linear optimization by a subclass of Riemannian metrics. Audience: The book is addressed to graduate students and researchers. The elementary notions necessary for understanding the material constitute part of the standard university curriculum.

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Smooth Nonlinear Optimization of Rn - Rapcsák, Tamás
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(*)
Rapcsák, Tamás:
Smooth Nonlinear Optimization of Rn - gebunden oder broschiert

1997, ISBN: 0792346807, Lieferbar binnen 4-6 Wochen

ID: 9780792346807

Internationaler Buchtitel. In englischer Sprache. Verlag: Springer-Verlag GmbH, HC runder Rücken kaschiert, 396 Seiten, L=235mm, B=155mm, H=26mm, Gew.=742gr, [GR: 16280 - HC/Mathematik/Wahrscheinlichkeitstheorie], [SW: - Differenzialgeometrie], Gebunden, Klappentext: This book is the first uniform, differential geometric approach to smooth nonlinear optimization. This advance allows the author to improve the sufficiency part of the Lagrange multiplier rule introduced in 1788 and to solve Fenchel's problem of level sets (1953) in the smooth case. Furthermore, this permits the author to replace convexity by geodesic convexity and apply it in complementarity systems, to study the nonlinear coordinate representations of smooth optimization problems, to describe the structure by tensors, to introduce a general framework for variable metric methods containing many basic nonlinear optimization algorithms, and - last but not least - to generate a class of polynomial interior point algorithms for linear optimization by a subclass of Riemannian metrics. Audience: The book is addressed to graduate students and researchers. The elementary notions necessary for understanding the material constitute part of the standard university curriculum.

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ID: 1865043&WAN=10022&WBT=28664&WMID=W000000443

1997. ; GEB ; Rapcsák:Smooth Nonlinear Optimization o This book is the first uniform, differential geometric approach to smooth nonlinear optimization. This advance allows the author to improve the sufficiency part of the Lagrange multiplier rule introduced in 1788 and to solve Fenchel's problem of level sets (1953) in the smooth case. Furthermore, this permits the author to replace convexity by geodesic convexity and apply it in complementarity systems, to study the nonlinear coordinate representations of smooth optimization problems, to describe the structure by tensors, to introduce a general framework for variable metric methods containing many basic nonlinear optimization algorithms, and - last but not least - to generate a class of polynomial interior point algorithms for linear optimization by a subclass of Riemannian metrics. Audience: The book is addressed to graduate students and researchers. The elementary notions necessary for understanding the material constitute part of the standard university curriculum. Buch gebund. Buch

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Smooth Nonlinear Optimization of RN - Tamas Rapcsak
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Tamas Rapcsak:
Smooth Nonlinear Optimization of RN - gebunden oder broschiert

1997, ISBN: 9780792346807

ID: 702995

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Details zum Buch
Smooth Nonlinear Optimization of Rn

This book is the first uniform, differential geometric approach to smooth nonlinear optimization. This advance allows the author to improve the sufficiency part of the Lagrange multiplier rule introduced in 1788 and to solve Fenchel's problem of level sets (1953) in the smooth case. Furthermore, this permits the author to replace convexity by geodesic convexity and apply it in complementarity systems, to study the nonlinear coordinate representations of smooth optimization problems, to describe the structure by tensors, to introduce a general framework for variable metric methods containing many basic nonlinear optimization algorithms, and - last but not least - to generate a class of polynomial interior point algorithms for linear optimization by a subclass of Riemannian metrics. Audience: The book is addressed to graduate students and researchers. The elementary notions necessary for understanding the material constitute part of the standard university curriculum.

Detailangaben zum Buch - Smooth Nonlinear Optimization of Rn


EAN (ISBN-13): 9780792346807
ISBN (ISBN-10): 0792346807
Gebundene Ausgabe
Erscheinungsjahr: 1997
Herausgeber: Springer-Verlag GmbH
396 Seiten
Gewicht: 0,742 kg
Sprache: eng/Englisch

Buch in der Datenbank seit 13.01.2008 17:19:34
Buch zuletzt gefunden am 30.11.2016 01:06:16
ISBN/EAN: 9780792346807

ISBN - alternative Schreibweisen:
0-7923-4680-7, 978-0-7923-4680-7


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