Wow! eBook
Matrix Computations with Python: A Practical Course for Physical and Data Sciences
Author(s): G.R. Liu (Author)
- Publisher: Scientech Publisher LLC
- Publication Date: August 24, 2026
- Language: English
- Print length: 357 pages
- ISBN-10: 194601818X
- ISBN-13: 9781946018182
Book Description
Modern science and engineering rely on solving large-scale linear systems, eigenvalue problems, and singular value decompositions efficiently. This book provides a practical, code-driven guide to modern numerical algorithms for tackle these high-dimensional matrix problems, with a strong focus on iterative methods, Krylov subspace projections, andrandomized techniques.
What You Will Learn:
• Linear Systems: Core iterative solvers including Conjugate Gradient (CG) and GMRES, paired with preconditioning strategies.
• Eigenvalue Problems: Essential algorithms spanning Power Iteration, QR, Lanczos,and Divide-and-Conquer approaches.
Singular Value Decomposition: High-impact SVD techniques, including Golub–KahanBidiagonalization, Randomized SVD, and the Randomized Nyström Method. An Interactive, Hands-On Approach: Designed for interactive learning, this text seamlessly bridges theory, mathematical formulation, and executable Python code within a Jupyter Notebook framework. Readers can directly execute code, tweak parameters, and analyze convergence through worked-out examples and real-world computational case studies. Who This Book Is For: An ideal resource for undergraduate and graduate students, researchers, and practicing engineers in scientific computing, computational engineering, applied mathematics, and data science seeking to master modern large-scale matrix solvers.
What You Will Learn:
• Linear Systems: Core iterative solvers including Conjugate Gradient (CG) and GMRES, paired with preconditioning strategies.
• Eigenvalue Problems: Essential algorithms spanning Power Iteration, QR, Lanczos,and Divide-and-Conquer approaches.
Singular Value Decomposition: High-impact SVD techniques, including Golub–KahanBidiagonalization, Randomized SVD, and the Randomized Nyström Method. An Interactive, Hands-On Approach: Designed for interactive learning, this text seamlessly bridges theory, mathematical formulation, and executable Python code within a Jupyter Notebook framework. Readers can directly execute code, tweak parameters, and analyze convergence through worked-out examples and real-world computational case studies. Who This Book Is For: An ideal resource for undergraduate and graduate students, researchers, and practicing engineers in scientific computing, computational engineering, applied mathematics, and data science seeking to master modern large-scale matrix solvers.
