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Book synopsis

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, and randomized 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-Kahan Bidiagonalization, 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.

Book details

• Full title: Matrix Computations with Python
• Subtitle: A Practical Course for Physical and Data Sciences
• Author: G.R. Liu
• Language: English
• Binding: Hardback
• Number of pages: 358
• EAN: 9781946018182
• ISBN: 194601818X
• ID: 54391287
• Publisher: Scientech Publisher LLC
• Weight: 838 g
• Dimensions: 254 × 178 × 21 mm
• Date of publishing: 15. September 2026

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