MATLAB Fundamentals for Applied Linear Algebra

Vector, Maxwell

ISBN 13: 9798281942041

Published by Independently published, 2025

About this Item

Seller Inventory # 50331492-n

Bibliographic Details

• Title: MATLAB Fundamentals for Applied Linear Algebra
• Publisher: Independently published
• Publication Date: 2025
• Language: English
• ISBN 13: 9798281942041
• Binding: Soft cover
• Condition: New

About this title

Synopsis

Ready to turn MATLAB into your personal powerhouse for conquering every core topic of applied linear algebra? This definitive reference distills the subject into 99 laser-focused chapters that walk you from scalar arithmetic to the Singular Value Decomposition—with code-ready examples at every step. Whether you’re an engineering student, data-science professional, or researcher hunting for rock-solid numerical methods, you’ll find the clarity, rigor, and hands-on practice that other texts leave out.

What Sets It Apart

• Step-by-Step MATLAB Walkthroughs – Instantly runnable scripts and functions illustrate each theorem, proof, and algorithm.
• Concept-First Structure – Pure mathematics first; syntax second, so you grasp why every command works.
• Comprehensive Coverage – Eigenvalues, LU/QR/Cholesky factorizations, least-squares, pseudoinverses, condition numbers, Kronecker products, sparse matrices, Gershgorin disks, Lyapunov and Sylvester equations, and much more.
• Zero Fluff – No installation guides, no GUI screenshots—just the ironclad math and code professionals need.
• Built for Real-World Impact – Optimize control systems, accelerate machine-learning pipelines, and bulletproof scientific computations.

Inside You’ll Discover

• Proven shortcuts for matrix factorization that slice runtime on large datasets
• How to diagnose ill-conditioned systems with operator norms and condition numbers
• Crafting orthonormal bases using Gram-Schmidt, Householder reflections, and Givens rotations
• Deploying the Singular Value Decomposition for dimensionality reduction and noise filtering
• Leveraging sparse and banded matrix structures to save gigabytes of memory

Unlock deeper intuition, write cleaner code, and solve bigger problems—faster.