Numerical algorithms : methods for computer vision, machine learning, and graphics
著者
書誌事項
Numerical algorithms : methods for computer vision, machine learning, and graphics
（An A K Peters book）
CRC Press, Taylor & Francis Group, 2020
- : pbk.
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注記
Originally published: 2015
"With VitalSource ebook"--Cover, spine, back cover.
Includes bibliographical references (p.361-368) and index.
内容説明・目次
内容説明
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.
目次
Preliminaries: Mathematics Review. Numerics and Error Analysis. Linear Algebra: Linear Systems and the LU Decomposition. Designing and Analyzing Linear Systems. Column Spaces and QR. Eigenvectors. Singular Value Decomposition. Nonlinear Techniques: Nonlinear Systems. Unconstrained Optimization. Constrained Optimization. Iterative Linear Solvers. Specialized Optimization Methods. Functions, Derivatives, and Integrals: Interpolation. Integration and Differentiation. Ordinary Differential Equations. Partial Differential Equations.
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