Parallel Computing in Optimization: Applied Optimization, cartea 7
Editat de A. Migdalas, Panos M. Pardalos, Sverre Storøyen Limba Engleză Paperback – 13 oct 2011
| Toate formatele și edițiile | Preț | Express |
|---|---|---|
| Paperback (1) | 1838.57 lei 6-8 săpt. | |
| Springer Us – 13 oct 2011 | 1838.57 lei 6-8 săpt. | |
| Hardback (1) | 1844.89 lei 6-8 săpt. | |
| Springer Us – 31 mai 1997 | 1844.89 lei 6-8 săpt. |
Din seria Applied Optimization
- 18%
Preț: 1185.68 lei -
Preț: 122.67 lei - 15%
Preț: 618.50 lei - 18%
Preț: 1183.54 lei - 20%
Preț: 941.67 lei - 15%
Preț: 613.62 lei - 15%
Preț: 621.67 lei - 18%
Preț: 1073.46 lei - 15%
Preț: 614.11 lei - 15%
Preț: 618.99 lei - 15%
Preț: 616.15 lei - 20%
Preț: 956.44 lei - 15%
Preț: 625.26 lei - 18%
Preț: 917.09 lei - 18%
Preț: 1192.50 lei - 18%
Preț: 1172.65 lei - 18%
Preț: 1187.36 lei - 18%
Preț: 1180.03 lei - 18%
Preț: 1183.40 lei - 18%
Preț: 922.11 lei - 15%
Preț: 629.35 lei - 18%
Preț: 916.33 lei - 18%
Preț: 912.40 lei - 15%
Preț: 617.89 lei - 15%
Preț: 618.50 lei - 15%
Preț: 623.05 lei - 15%
Preț: 614.41 lei
Preț: 1838.57 lei
Preț vechi: 2298.21 lei
-20% Nou
Puncte Express: 2758
Preț estimativ în valută:
325.33€ • 382.01$ • 285.56£
325.33€ • 382.01$ • 285.56£
Carte tipărită la comandă
Livrare economică 27 ianuarie-10 februarie 26
Preluare comenzi: 021 569.72.76
Specificații
ISBN-13: 9781461334026
ISBN-10: 1461334020
Pagini: 612
Ilustrații: XX, 588 p.
Dimensiuni: 160 x 240 x 32 mm
Greutate: 0.84 kg
Ediția:1997
Editura: Springer Us
Colecția Springer
Seria Applied Optimization
Locul publicării:New York, NY, United States
ISBN-10: 1461334020
Pagini: 612
Ilustrații: XX, 588 p.
Dimensiuni: 160 x 240 x 32 mm
Greutate: 0.84 kg
Ediția:1997
Editura: Springer Us
Colecția Springer
Seria Applied Optimization
Locul publicării:New York, NY, United States
Public țintă
ResearchCuprins
1 Models for Parallel Algorithm Design: An Introduction.- 1 Introduction.- 2 Shared memory model: PRAM.- 3 Distributed memory models: DMM.- 4 The coarse grained multicomputer model: CGM.- 5 Summary.- 6 Exercises.- 2 Parallel Algorithms and Complexity.- 1 Introduction.- 2 Models of Parallel Computers.- 3 Limits of Parallelism.- 4 Classification of some Important Graph Problems.- 5 Basic Techniques.- 6 Parallel Algorithms Toolbox.- 7 Approximating the Minimum Degree Spanning Tree Problem.- 8 Exercises.- 3 A Programmer’s View of Parallel Computers.- 1 Introduction.- 2 The Memory Hierarchy.- 3 Communication Network.- 4 Future trends.- 5 Exercises.- 4 Scalable Parallel Algorithms for Sparse Linear Systems.- 1 Introduction.- 2 Parallel Direct Cholesky Factorization.- 3 Multilevel Graph Partitioning.- 4 Exercises.- 5 Object Oriented Mathematical Modelling and Compilation to Parallel Code.- 1 Introduction.- 2 ObjectMath.- 3 Background to Parallel Code Generation.- 4 Definitions.- 5 Towards a Parallelising Compiler.- 6 Equation System Level.- 7 Equation Level.- 8 Clustered Task Level.- 9 Explicit Parallelism.- 10 Summary.- 11 Exercises.- 6 Parallel Algorithms for Network Problems.- 1 Introduction.- 2 Parallel processing paradigms.- 3 The shortest path problem.- 4 Linear problems over bipartite graphs.- 5 Convex problems over singlecommodity networks.- 6 Convex problems over multicommodity networks.- 7 Exercises.- 7 Parallel Branch and Bound — Principles and Personal Experiences.- 1 Introduction.- 2 Sequential B&B.- 3 Parallel B&B.- 4 Personal Experiences with GPP and QAP.- 5 Ideas and Pitfalls for Parallel B&B users.- 6 Exercises.- 8 Parallelized Heuristics for Combinatorial Search.- 1 Heuristics for Combinatorial Search.- 2 Local Search.- 3 Simulated Annealing.- 4 TabuSearch.- 5 Genetic Algorithms.- 6 Greedy Randomized Adaptive Search Procedures.- 7 Conclusions.- 8 Exercises.- 9 Parallel Cost Approximation Algorithms for Differentiable Optimization.- 1 Introduction.- 2 Sequential Cost Approximation Algorithms.- 3 Synchronized Parallel Cost Approximation Algorithms.- 4 Partially Asynchronous Parallel Cost Approximation Algorithms.- 5 Concluding Remarks.- 6 Exercises.- 10 Parallel Computation of Variational Inequalities and Projected Dynamical Systems with Applications.- 1 Introduction.- 2 The Variational Inequality Problem.- 3 Projected Dynamical Systems.- 4 Variational Inequality Applications.- 5 Projected Dynamical Systems Applications.- 6 Summary and Conclusions.- 7 Exercises.- 11 Parallel Algorithms for Large-Scale Stochastic Programming.- 1 Introduction.- 2 Stochastic Programs with Recourse.- 3 Algorithmic Approaches.- 4 Algorithmic Comparisons.- 5 Conclusions.- 6 Exercises.- 12 Parallel Continuous Non-Convex Optimization.- 1 Introduction.- 2 Local Search Heuristics.- 3 Deterministic and Stochastic Refinements of Local Search.- 4 Summary of General Principles for Local Search Parallelization.- 5 Exact Methods: Deterministic Approaches.- 6 Exercises.- 13 Deterministic and Stochastic Logarithmic Barrier Function Methods for Neural Network Training.- 1 Introduction.- 2 Newton-type and Logarithmic Barrier Methods.- 3 Application to Neural Network Training.- 4 Ill-Conditioning.- 5 Computational Results.- 6 Conclusions and Future Research.- 7 Exercises.