Smooth Nonlinear Optimization in Rn: Nonconvex Optimization and Its Applications, cartea 19
Autor Tamás Rapcsáken Limba Engleză Hardback – 31 aug 1997
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Springer Us – 31 aug 1997 | 1227.67 lei 43-57 zile |
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Specificații
ISBN-13: 9780792346807
ISBN-10: 0792346807
Pagini: 396
Ilustrații: XIV, 376 p.
Dimensiuni: 156 x 234 x 27 mm
Greutate: 0.73 kg
Ediția:1997
Editura: Springer Us
Colecția Springer
Seria Nonconvex Optimization and Its Applications
Locul publicării:New York, NY, United States
ISBN-10: 0792346807
Pagini: 396
Ilustrații: XIV, 376 p.
Dimensiuni: 156 x 234 x 27 mm
Greutate: 0.73 kg
Ediția:1997
Editura: Springer Us
Colecția Springer
Seria Nonconvex Optimization and Its Applications
Locul publicării:New York, NY, United States
Public țintă
ResearchCuprins
Preface. 1. Introduction. 2. Nonlinear Optimization Problems. 3. Optimality Conditions. 4. Geometric Background of Optimality Conditions. 5. Deduction of the Classical Optimality Conditions in Nonlinear Optimization. 6. Geodesic Convex Functions. 7. On the Connectedness of the Solution Set to Complementarity Systems. 8. Nonlinear Coordinate Representations. 9. Tensors in Optimization. 10. Geodesic Convexity on R°n+ 11. Variable Metric Methods Along Geodesics. 12. Polynomial Variable Metric Methods for Linear Optimization. 13. Special Function Classes. 14. Fenchel's Unsolved Problem of Level Sets. 15. An Improvement of the Lagrange Multiplier Rule for Smooth Optimization Problems. A. On the Connection Between Mechanical Force Equilibrium and Nonlinear Optimization. B. Topology. C. Riemannian Geometry. References. Author Index. Subject Index. Notations.
Recenzii
`... it is a pleasure to see and read how Tamás Rapcsák combines differential geometry and other high-level mathematics to model and solve problems which belong to smooth nonlinear optimization. The book can be highly recommended to researchers and graduate students interested in optimization theory. In summary, this book is a vaulable contribution to the existing literature on smooth optimization in finite dimension.'
Optimization, 46 (1999)
Optimization, 46 (1999)