Cambridge Monographs on Applied and Computational Mathematics Series

Focusing on the Schwarz-Christoffel transformation, this book delves into its historical background, foundational principles, and a range of practical computations. It explores various applications in diverse fields such as electromagnetism and fluid flow, making it a valuable resource for engineers, scientists, and applied mathematicians. Theoretical results are clearly stated and proved, with an emphasis on practical understanding. Additionally, it includes a brief appendix on the Schwarz-Christoffel Toolbox for MATLAB, enhancing computational techniques for conformal mapping.

This book offers a comprehensive introduction to scattered data approximation theory, making it an ideal resource for graduate students and researchers. It covers essential concepts and methodologies, providing a solid foundation for understanding the subject. The text is designed to be self-contained, ensuring accessibility for those new to the field while also serving as a valuable reference for experienced practitioners.

The book offers a comprehensive exploration of the theoretical foundations essential for understanding algorithms in numerical mathematics. It covers both classical results and the latest advancements in the field, making it a valuable resource for those seeking to deepen their knowledge of numerical algorithms and their applications.

Combining geometry, topology, algorithms, and engineering, this graduate text emphasizes essential and practical topics. It serves as a comprehensive resource for students, focusing on foundational concepts that are applicable in various fields, making it both accessible and valuable for advanced study.

The book offers a comprehensive guide to the numerical methods used for solving a wide range of integral equations. It covers various techniques and approaches, making it a valuable resource for those looking to understand and apply these mathematical concepts effectively.

Sure to be influential, Watanabe’s book lays the foundations for the use of algebraic geometry in statistical learning theory. Many models/machines are mixture models, neural networks, HMMs, Bayesian networks, stochastic context-free grammars are major examples. The theory achieved here underpins accurate estimation techniques in the presence of singularities.