Parallel Computing in Optimization: Applied Optimization, cartea 7
Editat de A. Migdalas, Panos M. Pardalos, Sverre Storøyen Limba Engleză Hardback – 31 mai 1997
Din seria Applied Optimization
-
Preț: 125.26 lei - 15%
Preț: 615.43 lei - 18%
Preț: 1183.54 lei - 18%
Preț: 1170.76 lei - 20%
Preț: 962.79 lei - 15%
Preț: 611.17 lei - 15%
Preț: 636.77 lei - 18%
Preț: 1097.89 lei - 15%
Preț: 611.78 lei - 15%
Preț: 618.99 lei - 15%
Preț: 614.12 lei - 20%
Preț: 956.44 lei - 15%
Preț: 625.26 lei - 15%
Preț: 618.25 lei - 18%
Preț: 1212.82 lei - 24%
Preț: 858.21 lei - 18%
Preț: 1200.75 lei - 18%
Preț: 1162.47 lei - 18%
Preț: 1187.36 lei - 18%
Preț: 1278.61 lei - 18%
Preț: 1183.40 lei - 18%
Preț: 916.19 lei - 15%
Preț: 626.93 lei - 18%
Preț: 908.18 lei - 18%
Preț: 934.34 lei - 15%
Preț: 611.89 lei - 15%
Preț: 614.77 lei - 15%
Preț: 613.77 lei - 18%
Preț: 1176.26 lei - 15%
Preț: 615.92 lei - 15%
Preț: 623.52 lei - 18%
Preț: 916.33 lei - 18%
Preț: 1329.74 lei - 18%
Preț: 1185.38 lei
Preț: 1885.75 lei
Preț vechi: 2357.18 lei
-20%
Puncte Express: 2829
Carte tipărită la comandă
Livrare economică 27 iulie-10 august
Livrare prin curier în România Termenul estimat este afișat lângă disponibilitate.
Transport gratuit pentru acest produs Plată online sau ramburs, în funcție de opțiunile comenzii.
Retur gratuit în 14 zile Comandă securizată și suport în română.
Specificații
ISBN-13: 9780792345831
ISBN-10: 0792345835
Pagini: 612
Ilustrații: XX, 588 p.
Dimensiuni: 160 x 241 x 38 mm
Greutate: 1.07 kg
Ediția:1997
Editura: Springer
Colecția Applied Optimization
Seria Applied Optimization
Locul publicării:New York, NY, United States
ISBN-10: 0792345835
Pagini: 612
Ilustrații: XX, 588 p.
Dimensiuni: 160 x 241 x 38 mm
Greutate: 1.07 kg
Ediția:1997
Editura: Springer
Colecția Applied Optimization
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.