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This book presents the mathematics of quantum computation. The purpose is to introduce the topic of quantum computing to students in computer science, physics and mathematics who have no prior knowledge of this field.

The topics include essential, advanced mathematics to access as a refresher or to add to the mathematical education and address the needs of engineers and scientists who need to expand their working knowledge. These topics are driven by applications and exercises with solutions are offered to confirm understanding.

The book includes contributions by top researchers offering topics associated with equations and their relevance and significance in various scientific areas of study and research. The readers will find several important and useful methods and techniques for solving various types of fractional-order models in engineering and science.

This book develops foundational concepts in probability and statistics with primary applications in mechanical and aerospace engineering. It was designed utilizing the latest research in statistics learning and in engagement teaching practices.

The book is a compilation of methods for solving and approximating differential equations. These include the most widely applicable methods for solving and approximating differential equations, as well as numerous methods. Topics include methods for ODEs, PDEs, stochastic DEs, and systems of such equations.

Quadratic programming is a mathematical technique that allows for the optimization of a quadratic function in several variables. QP is a subset of Operations Research and is the next higher lever of sophistication than Linear Programming. It is a key mathematical tool in Portfolio Optimization and structural plasticity. This is useful in Civil Engineering as well as Statistics.

This textbook presents a strong and clear relationship between theory and practice. It covers basic topics such as Dantzig¿s simplex algorithm, duality, sensitivity analysis, integer optimization models, and network models as well as more advanced topics including interior point algorithms, the branch-and-bound algorithm, cutting planes, and complexity. Along with case studies, it also discusses more advanced techniques such as column generation, multiobjective optimization, and game theory. It also includes computer code in the form of models in GMPL. The book contains appendices covering mathematical proofs, linear algebra, graph theory, convexity, and a background in nonlinear optimization. All chapters contain extensive examples and exercises. .

This text fills a niche between a calculus-based probability course and a stochastic processes course typically taken by upper undergraduate students. While Markov processes are touched on in probability courses, this book offers the opportunity to concentrate on the topic when additional study is required. It discusses how Markov processes are applied in a number of fields, including economics, physics, and mathematical biology.

Presents an easy-to-read introduction to the basic ideas and techniques of game theory. This book covers combinatorial games. It also focuses on two-person zero-sum games and their solution. It presents the simplex method, based on linear programming, for solving these games and develops the required background in linear programming.

Covering the main fields of mathematics, this handbook focuses on the methods used for obtaining solutions of various classes of mathematical equations that underlie the mathematical modeling of numerous phenomena and processes in science and technology.

Containing over 6,000 entries, the newest edition of this popular series continues to provide essential formulas, tables, figures and detailed descriptions. Plus, it features many diagrams, group tables, and integrals not available online.It also incorporates topics such as max plus algebra, financial options, pseudospectra, and proof methods.

This book provides a systematic approach to the various methods available for deriving a Green¿s function. It begins by reviewing the historical development of the Green¿s function, the Fourier and Laplace transforms, the classical special functions of Bessel functions and Legendre polynomials, and the Dirac delta function. It then presents Green¿s functions for each class of differential equation (ordinary differential, wave, heat, and Helmholtz equations) according to the number of spatial dimensions and the geometry of the domain, including worked examples, problem sets, and illustrations.

A guide to the use of operations research (OR) techniques in decision-making, design, analysis, and management. It examines methods and practical issues related to the development and use of computer implementations. It offers information on how to select and obtain software and describes its important characteristics.

This book provides a systematic approach to the various methods available for deriving a Green¿s function. It begins by reviewing the historical development of the Green¿s function, the Fourier and Laplace transforms, the classical special functions of Bessel functions and Legendre polynomials, and the Dirac delta function. It then presents Green¿s functions for each class of differential equation (ordinary differential, wave, heat, and Helmholtz equations) according to the number of spatial dimensions and the geometry of the domain, including worked examples, problem sets, and illustrations.

With an introduction to signal processing, this book outlines the fundamental principles and applications of radar. It covers the basics of radar, includes important mathematical derivations, and discusses research and industry trends along with a range of advanced topics and radar technology issues.