Numerical Algorithms Methods for Computer Vision Machine Learning and Graphics – 1st Edition

About the Author

Justin Solomon is an X-Consortium Career Development Assistant Professor in MIT’s Department of Electrical Engineering and Computer Science (EECS).  Solomon leads MIT’s Geometric Data Processing Group, which studies problems in shape analysis, machine learning, and graphics from a geometric perspective.  Before coming to MIT, he was an NSF Mathematical Sciences Postdoctoral Fellow in Princeton’s Program in Applied and Computational Mathematics.  He received a PhD in computer science from Stanford University, where he was also a lecturer for courses in graphics, differential geometry, and numerical methods. Before his graduate studies, he was a member of Pixar’s Tools Research group.


Product details


A K Peters/CRC Press






1 edition
July 13, 2015

Page Count


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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