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In this introductory text, the fundamental algorithms of numerical linear algebra are developed in a parallel context.
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In this introductory text, the fundamental algorithms of numerical linear algebra are developed in a parallel context.
Produktdetails
- Produktdetails
- Verlag: Cambridge University Press
- Seitenzahl: 306
- Erscheinungstermin: 26. Oktober 2006
- Englisch
- Abmessung: 229mm x 152mm x 18mm
- Gewicht: 500g
- ISBN-13: 9780521683371
- ISBN-10: 0521683378
- Artikelnr.: 21861130
- Verlag: Cambridge University Press
- Seitenzahl: 306
- Erscheinungstermin: 26. Oktober 2006
- Englisch
- Abmessung: 229mm x 152mm x 18mm
- Gewicht: 500g
- ISBN-13: 9780521683371
- ISBN-10: 0521683378
- Artikelnr.: 21861130
Ronald W. Shonkwiler is a Professor in the School of Mathematics at the Georgia Institute of Technology. He has authored or co-authored over 50 research papers in areas of functional analysis, mathematical biology, image processing algorithms, fractal geometry, neural networks and Monte Carlo optimization methods. His algorithm for monochrome image comparison is part of a US patent for fractal image compression. He has co-authored two other books, An Introduction to the Mathematics of Biology and The Handbook of Stochastic Analysis and Applications.
Part I. Machines and Computation: 1. Introduction - the nature of high performance computation
2. Theoretical considerations - complexity
3. Machine implementations
Part II. Linear Systems: 4. Building blocks - floating point numbers and basic linear algebra
5. Direct methods for linear systems and LU decomposition
6. Direct methods for systems with special structure
7. Error analysis and QR decomposition
8. Iterative methods for linear systems
9. Finding eigenvalues and eigenvectors
Part III. Monte Carlo Methods: 10. Monte Carlo simulation
11. Monte Carlo optimization
Appendix: programming examples.
2. Theoretical considerations - complexity
3. Machine implementations
Part II. Linear Systems: 4. Building blocks - floating point numbers and basic linear algebra
5. Direct methods for linear systems and LU decomposition
6. Direct methods for systems with special structure
7. Error analysis and QR decomposition
8. Iterative methods for linear systems
9. Finding eigenvalues and eigenvectors
Part III. Monte Carlo Methods: 10. Monte Carlo simulation
11. Monte Carlo optimization
Appendix: programming examples.
Part I. Machines and Computation: 1. Introduction - the nature of high performance computation
2. Theoretical considerations - complexity
3. Machine implementations
Part II. Linear Systems: 4. Building blocks - floating point numbers and basic linear algebra
5. Direct methods for linear systems and LU decomposition
6. Direct methods for systems with special structure
7. Error analysis and QR decomposition
8. Iterative methods for linear systems
9. Finding eigenvalues and eigenvectors
Part III. Monte Carlo Methods: 10. Monte Carlo simulation
11. Monte Carlo optimization
Appendix: programming examples.
2. Theoretical considerations - complexity
3. Machine implementations
Part II. Linear Systems: 4. Building blocks - floating point numbers and basic linear algebra
5. Direct methods for linear systems and LU decomposition
6. Direct methods for systems with special structure
7. Error analysis and QR decomposition
8. Iterative methods for linear systems
9. Finding eigenvalues and eigenvectors
Part III. Monte Carlo Methods: 10. Monte Carlo simulation
11. Monte Carlo optimization
Appendix: programming examples.