This book is a solid foundation of the most important formalisms used for specification and verification of reactive systems. In particular, the text presents all important results on m-calculus, w-automata, and temporal logics, shows the relationships between these formalisms and describes state-of-the-art verification procedures for them. It also discusses advantages and disadvantages of these formalisms, and shows up their strengths and weaknesses. Most results are given with detailed proofs, so that the presentation is almost self-contained. Includes all definitions without relying on other material Proves all theorems in detail Presents detailed algorithms in pseudo-code for verification as well as translations to other formalisms
Rapid development of computing power of personal computers, workstations, mainframes, super computers and integrated circuits has provided scientists and engineers with powerful tools in solving their scientific problems using computers, and is expected to continue to increase well in the future. The monograph mainly contains the following three parts: analysis of supremum of weighted pseudoinverses, study the stability of weighted pseudoinverses, weighted least squares problems and constrained weighted least squares problems, and stable methods for solving weighted least squares problems and constrained weighted least squares problems.
The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site.
These volumes replace the 1933 Supplement to the OED. The vocabulary treated is that which came into use during the publication of the successive sections of the main Dictionary -- that is, between 1884, when the first fascicle of the letter A was published, and 1928, when the final section of the Dictionary appeared -- together with accessions to the English language in Britain and abroad from 1928 to the present day. Nearly all the material in the 1933 Supplement has been retained here, though in revised form (Preface).
With this second volume, we enter the intriguing world of complex analysis. From the first theorems on, the elegance and sweep of the results is evident. The starting point is the simple idea of extending a function initially given for real values of the argument to one that is defined when the argument is complex. From there, one proceeds to the main properties of holomorphic functions, whose proofs are generally short and quite illuminating: the Cauchy theorems, residues, analytic continuation, the argument principle. With this background, the reader is ready to learn a wealth of additional material connecting the subject with other areas of mathematics: the Fourier transform treated by contour integration, the zeta function and the prime number theorem, and an introduction to elliptic functions culminating in their application to combinatorics and number theory. Thoroughly developing a subject with many ramifications, while striking a careful balance between conceptual insights and the technical underpinnings of rigorous analysis, Complex Analysis will be welcomed by students of mathematics, physics, engineering and other sciences. The Princeton Lectures in Analysis represents a sustained effort to introduce the core areas of mathematical analysis while also illustrating the organic unity between them. Numerous examples and applications throughout its four planned volumes, of which Complex Analysis is the second, highlight the far-reaching consequences of certain ideas in analysis to other fields of mathematics and a variety of sciences. Stein and Shakarchi move from an introduction addressing Fourier series and integrals to in-depth considerations of complex analysis; measure and integration theory, and Hilbert spaces; and, finally, further topics such as functional analysis, distributions and elements of probability theory.
Milo's mum looks like any ordinary mum... She makes lovely cakes, she has a nice smile and she wears sensible shoes. But no one knows she actually has x-ray vision! She can see everything and knows everything Milo is doing even when she's not there... Is Milo's mum a supermum?
