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Attraction in Numerical Minimization: Iteration Mappings, Attractors, and Basins of Attraction 2018 ed. [Minkštas viršelis]

  • Formatas: Paperback / softback, 78 pages, aukštis x plotis: 235x155 mm, weight: 454 g, 49 Illustrations, color; XII, 78 p. 49 illus. in color., 1 Paperback / softback
  • Serija: SpringerBriefs in Optimization
  • Išleidimo metai: 19-Dec-2018
  • Leidėjas: Springer Nature Switzerland AG
  • ISBN-10: 3030040488
  • ISBN-13: 9783030040482
Kitos knygos pagal šią temą:
  • Formatas: Paperback / softback, 78 pages, aukštis x plotis: 235x155 mm, weight: 454 g, 49 Illustrations, color; XII, 78 p. 49 illus. in color., 1 Paperback / softback
  • Serija: SpringerBriefs in Optimization
  • Išleidimo metai: 19-Dec-2018
  • Leidėjas: Springer Nature Switzerland AG
  • ISBN-10: 3030040488
  • ISBN-13: 9783030040482
Kitos knygos pagal šią temą:
Numerical minimization of an objective function is analyzed in this book to understand solution algorithms for optimization problems. Multiset-mappings are introduced to engineer numerical minimization as a repeated application of an iteration mapping. Ideas from numerical variational analysis are extended to define and explore notions of continuity and differentiability of multiset-mappings, and prove a fixed-point theorem for iteration mappings. Concepts from dynamical systems are utilized to develop notions of basin size and basin entropy.  Simulations to estimate basins of attraction, to measure and classify basin size, and to compute basin are included to shed new light on convergence behavior in numerical minimization.





Graduate students, researchers, and practitioners in optimization and mathematics who work theoretically to develop solution algorithms will find this book a useful resource.

Recenzijos

This book is aimed at researchers and practitioners working in the area of numerical minimization. (Olga Brezhneva, Mathematical Reviews, January, 2020)

1 Multisets and Multiset Mappings
1(10)
1.1 Pre-distance Functions
2(2)
1.2 Calmness
4(4)
1.2.1 Calmness for Set-Valued Mappings
6(2)
1.3 Derivative Characterization of Calmness
8(3)
2 Iteration Mappings
11(12)
2.1 Coordinate-Search
12(1)
2.1.1 Iteration Mapping ICS, f
12(1)
2.2 Steepest-Descent
13(3)
2.2.1 Iteration Mapping ISDi, f
13(3)
2.3 Nelder--Mead
16(4)
2.3.1 Iteration Mapping INM, f
18(2)
2.4 Practical Considerations
20(1)
2.5 Convergence Theorems for Iteration Mappings
21(2)
3 Equilibria in Dynamical Systems
23(10)
3.1 Basins of Attraction
24(5)
3.1.1 Basin Size
25(2)
3.1.2 Basin Entropy
27(2)
3.2 Stability
29(1)
3.3 Equilibria as Minimizers
30(3)
4 Attractors
33(10)
4.1 Local Dense Viability
34(1)
4.2 Basins of Attraction
34(1)
4.3 Stability
35(2)
4.4 Minvalue-Monotonicity
37(1)
4.5 Attractors as Minimizers
38(5)
5 Basin Analysis via Simulation
43
5.1 Example 1: Two Global Minimizers
45(6)
5.1.1 Simulated Basins
46(2)
5.1.2 Basin Sizes
48(2)
5.1.3 Basin Entropy
50(1)
5.2 Example 2: One Global Minimizer and a Saddle Point
51(6)
5.2.1 Simulated Basins
52(1)
5.2.2 Basin Sizes
53(3)
5.2.3 Basin Entropy
56(1)
5.3 Example 3: One Global Minimizer and One Local Minimizer
57(5)
5.3.1 Simulated Basins
58(1)
5.3.2 Basin Sizes
58(3)
5.3.3 Basin Entropy
61(1)
5.4 Example 4: One Global Minimizer and a Ring of Local Minimizers
62(4)
5.4.1 Simulated Basins
63(1)
5.4.2 Basin Sizes
64(1)
5.4.3 Basin Entropy
64(2)
5.5 Nelder-Mead Method
66(5)
5.5.1 Simulated Basins
66(2)
5.5.2 Basin Sizes
68(1)
5.5.3 Basin Entropy
69(2)
5.6 Practical Significance of Counterexamples
71(6)
5.6.1 Coordinate-Search and the Canoe Function
71(3)
5.6.2 The Nelder--Mead Method and McKinnon's Function
74(3)
References
77