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MATLAB Fundamentals for Applied Linear Algebra

by Maxwell Vector
Sold out
Current price ₹3,399.00
Original price ₹3,773.00
Original price ₹3,773.00
Original price ₹3,773.00
(-10%)
₹3,399.00
Current price ₹3,399.00

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Book cover type: Paperback
  • ISBN13: 9798281942041
  • Binding: Paperback
  • Subject: N/A
  • Publisher: Independently Published
  • Publisher Imprint: Independently Published
  • Publication Date:
  • Pages: 514
  • Original Price: GBP 29.82
  • Language: English
  • Edition: N/A
  • Item Weight: 681 grams
  • BISAC Subject(s): Algebra / Linear

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.

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