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

Most existing textbooks on this subject were written either for mathematics or engineering students and do not address the unique situation of computer science students, who have some background in discrete mathematics but less familiarity with continuous methods of proof and algorithms. This book is written specifically for those students, filled throughout with practical examples using the algorithms being taught. The book also teaches theory in the context of application in parallel by delving into real-world problems in computer graphics and computer vision.

Justin Solomon is an NSF Mathematical Sciences Postdoctoral Fellow at Princeton’s Program in Applied and Computational Mathematics, where he is studying problems in shape analysis, machine learning, and graphics from a geometric perspective. 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.