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Matrix Mathematics A Second Course in Linear Algebra

Authors

Stephan Ramon Garcia, Pomona College, California, Roger A. Horn, University of Utah
Published 2023

Description

Using a modern matrix-based approach, this rigorous second course in linear algebra helps upper-level undergraduates in mathematics, data science, and the physical sciences transition from basic theory to advanced topics and applications. Its clarity of exposition together with many illustrations, 900+ exercises, and 350 conceptual and numerical examples aid the student's understanding. Concise chapters promote a focused progression through essential ideas. Topics are derived and discussed in detail, including the singular value decomposition, Jordan canonical form, spectral theorem, QR factorization,…

New in this edition

*Every chapter has been edited to reflect classroom experience

*300+ new problems and many more conceptual and numerical examples.

*Features new material on matrix norms, positive/nonnegative matrices, polynomial interpolation, Gaussian quadrature, orthogonal polynomials, and the Google matrix.

*More than 90 color-enhanced figures illustrate the geometric foundations of linear algebra.

*A comprehensive index, now with 2,000+ entries, enhances the value of the book for reference.

*Complete solutions to all problems are available in an Instructor’s Solution Manual. 

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Key features

  • Emphasizes matrix factorizations such as unitary triangularization, QR factorizations, spectral theorem, and singular value decomposition
  • Covers all relevant linear algebra material that students need to move on to advanced work in data science, such as convex optimization
  • Supplies 900 end-of-chapter problems, 350 conceptual and numerical examples, and many color illustrations to aid student understanding of concepts and help develop communication skills
  • Contains clear exposition and concise chapters that promote focused progression through essential ideas

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