Nonstandard Interaction Domains and Finite Element Discretizations
The book presents the state of the art of nonlocal modeling and discretization and novel analyses of a class of nonstandard nonlocal models. These models have recently become a viable alternative to classical partial differential equations when the latter are unable to capture effects such as discontinuities and multiscale behavior in a system of ......
Uncertainty quantification is an important step in establishing the predictive accuracy of simulation models employed in a broad range of disciplines. The book provides a comprehensive and unified treatment of the mathematical, statistical, and numerical topics required to perform uncertainty analysis for models arising in a wide range of ......
The method of least squares, discovered by Gauss in 1795, is a principal tool for reducing the influence of errors when fitting a mathematical model to given observations. Applications arise in a great number of areas in sciences and engineering. The increased use of automatic data capturing frequently leads to large-scale least squares problems. ......
Set-valued, convex, and nonsmooth analysis are relatively modern branches of mathematical analysis and increasingly relevant in current control theory and control engineering literature. The first book of this scope and at this level, Set-Valued, Convex, and Nonsmooth Analysis in Dynamics and Control serves as a broad introduction to the field and ......
The goal of Algorithmic Mathematics in Machine Learning is to explore several well-known machine learning and data analysis algorithms from a mathematical and programming perspective. In this unique book, the authors: Present machine learning methods, review the underlying mathematics, and provide programming exercises intended to deepen the ......
This textbook covers the latest advances in machine-learning methods for asset management and asset pricing. Recent research in deep learning applied to finance shows that some of the techniques used by asset managers (usually kept confidential) result in better investments than the more standard techniques. Cutting-edge material is integrated ......
How I Learned to Stop Worrying and Love Measure Theory
Measure theory and measure-theoretic probability are fascinating subjects. Proofs describing profound ways to reason lead to results that are frequently startling, beautiful, and useful. Measure theory and probability also play roles in the development of pure and applied mathematics, statistics, engineering, physics, and finance. Indeed, it is ......
The conjugate gradient (CG) algorithm is almost always the iterative method of choice for solving linear systems with symmetric positive definite matrices. This book describes and analyzes techniques based on Gauss quadrature rules to cheaply compute bounds on norms of the error. The techniques can be used to derive reliable stopping criteria. ......
This book uses Python to teach mathematics not found in the standard curriculum, so students learn a popular programming language as well as some interesting mathematics. Videos, images, programs, programming activities, pencil-and-paper activities, and associated Jupyter Notebooks accompany the text, and readers are encouraged to interact with ......
