Numerical Algorithms: Methods for Computer Vision, Machine Learning, and Graphics

1st Edition

Justin Solomon

VitalSource eBook access code and instructions will be provided within the print book.

A K Peters/CRC Press
Published July 13, 2015
Textbook - 400 Pages - 132 B/W Illustrations
ISBN 9781482251883 - CAT# K23847

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  • Contains classroom-tested material for a one- to two-semester course in numerical algorithms, with a focus on modeling and applications
  • Introduces themes common to nearly all classes of numerical algorithms
  • Covers algorithms for solving linear and nonlinear problems, including popular techniques recently introduced in the research community
  • Includes comprehensive end-of-chapter exercises that push students at all levels to derive, extend, and analyze numerical algorithms

Solutions manual and figure slides available upon qualifying course adoption


Numerical Algorithms: Methods for Computer Vision, Machine Learning, and Graphics presents a new approach to numerical analysis for modern computer scientists. Using examples from a broad base of computational tasks, including data processing, computational photography, and animation, the textbook introduces numerical modeling and algorithmic design from a practical standpoint and provides insight into the theoretical tools needed to support these skills.

The book covers a wide range of topics—from numerical linear algebra to optimization and differential equations—focusing on real-world motivation and unifying themes. It incorporates cases from computer science research and practice, accompanied by highlights from in-depth literature on each subtopic. Comprehensive end-of-chapter exercises encourage critical thinking and build students’ intuition while introducing extensions of the basic material.

The text is designed for advanced undergraduate and beginning graduate students in computer science and related fields with experience in calculus and linear algebra. For students with a background in discrete mathematics, the book includes some reminders of relevant continuous mathematical background.


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