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Books in Mathematics

The Mathematics collection presents a range of foundational and advanced research content across applied and discrete mathematics, including fields such as Computational Mathematics; Differential Equations; Linear Algebra; Modelling & Simulation; Numerical Analysis; Probability & Statistics.

BooksJournals

    • Extended Hypergeometric Functions and Orthogonal Polynomials

      • 1st Edition
      • February 1, 2026
      • Praveen Agarwal + 1 more
      • English
      • Paperback
        9 7 8 0 4 4 3 3 6 4 8 4 6
      • eBook
        9 7 8 0 4 4 3 3 6 4 8 5 3
      Extended Hypergeometric Functions and Orthogonal Polynomials presents a comprehensive and accessible resource for researchers and graduate students interested in exploring the rich connections between extended hypergeometric functions, orthogonal polynomials, and multivariable polynomials. Integrating all three fields and their applications in Maple, Mathematica, and MATLAB, this book fosters interdisciplinary understanding and inspires new avenues of research in mathematics, engineering, physics, and computer science. It also provides a glimpse into future research directions in these areas, including potential applications in emerging fields of applied mathematics and interdisciplinary collaborations. Each chapter begins with an introduction, includes sections on theory, followed by sections on applications, and ends with exercises, problems, references and suggested readings.

    • Multidimensional Signal Processing

      • 1st Edition
      • Volume 54
      • February 1, 2026
      • English
      • Hardback
        9 7 8 0 4 4 3 4 1 4 6 5 7
      • eBook
        9 7 8 0 4 4 3 4 1 4 6 6 4
      Multidimensional Signal Processing volume of the “Handbook of Statistics” series isdedicated to presenting the latest developments and methodologies in multidimensional signalprocessing. The book aims to provide a comprehensive overview of the theories, models, andmethods that form the foundation of this field. Our goal is to cater to a broad audience of signal-processing practitioners. The chapters within this volume are meticulously curated to offerdetailed, educational content rather than conventional journal-style articles. Contributors to thisvolume are leading experts in the field, ensuring high-profile content that combines theoreticalrigor with practical insights. Through this comprehensive compilation, we aim to advance theknowledge and application of multidimensional signal processing, contributing significantly tothe broader fields of statistics and data science.

    • Implementing R for Statistics

      • 1st Edition
      • January 19, 2026
      • Muhammad Imran + 3 more
      • English
      • Paperback
        9 7 8 0 4 4 3 3 8 3 2 1 2
      • eBook
        9 7 8 0 4 4 3 3 8 3 2 2 9
      Implementing R for Statistics is a timely first edition textbook that provides comprehensive coverage of basic statistical concepts using R, an important open-source programming language tool. This book guides the readers through understanding the R basics, from installing R and RStudio to exploring its basic structure and uses, to implementing and practicing these in statistics. Using the sophisticated and elegant data visualization of R, this book explains the latest advances in the R programming language using illustrated examples carefully designed to apply the core principles discussed in the text to emerging topics in the field. It also provides useful insights into the process of developing R packages and creating metadata, focusing on exploiting the flexible and user-friendly nature of R. Written by an international and experienced team of authors, Implementing R for Statistics is an essential text for students, lecturers, data scientists, and applied researchers in all areas of statistics and related fields such as biostatistics, health care, finance, risk management, social sciences, market research, and environmental and climate research.

    • An Introduction to Point-Set Topology

      • 1st Edition
      • January 1, 2026
      • Shelby J. Kilmer
      • English
      • Paperback
        9 7 8 0 4 4 3 4 1 4 0 1 5
      • eBook
        9 7 8 0 4 4 3 4 1 4 0 2 2
      An Introduction to Point-Set Topology is intended for use in a beginning topology course for undergraduates or as an elective course for graduate students. The book’s style can be thought of as a hybrid between the Texas style (Moore method) of teaching topology and the more traditional styles. In the Texas style the students are given the definitions and the statements of the theorems and then they present their proofs to the class. This type of participation builds students’ confidence and provides them with a deeper understanding of the subject that they will retain longer. This text offers some of the theorems with their proofs and leaves others for the students to prove and present. Those theorems chosen to have their proofs presented in the text keep the course moving forward under the instructors’ guidance and increase student comprehension. An Introduction to Point-Set Topology covers a broad range of topological concepts, including but not limited to, metric spaces, topological spaces, homeomorphisms, connected sets, compact sets, product spaces, Hausdorff spaces, sequences, limits, weak topologies, the axiom of choice, Zorn’s lemma, and Nets. Incorporating both historical references and color graphics, the material keeps readers engaged. The book’s goals include increasing student participation, thus promoting a deeper knowledge through an intuitive understanding of how and why topology was developed in the way that it was. This “instructor-friendly... accessible text is also accompanied by a detailed solutions manual to support both experienced topologists and other mathematicians who would like to teach topology.

    • Machine Learning Solutions for Inverse Problems: Part A

      • 1st Edition
      • Volume 26
      • October 14, 2025
      • English
      • Hardback
        9 7 8 0 4 4 3 4 1 7 8 9 4
      • eBook
        9 7 8 0 4 4 3 4 1 7 9 0 0
      Machine Learning Solutions for Inverse Problems: Part A, Volume 26 in the Handbook of Numerical Analysis, highlights new advances in the field, with this new volume presenting interesting chapters on a variety of timely topics, including Data-Driven Approaches for Generalized Lasso Problems, Implicit Regularization of the Deep Inverse Prior via (Inertial) Gradient Flow, Generalized Hardness of Approximation, Hallucinations, and Trustworthiness in Machine Learning for Inverse Problems, Energy-Based Models for Inverse Imaging Problems, Regularization Theory of Stochastic Iterative Methods for Solving Inverse Problems, and more.Other sections cover Advances in Identifying Differential Equations from Noisy Data Observations, The Complete Electrode Model for Electrical Impedance Tomography: A Comparative Study of Deep Learning and Analytical Methods, Learned Iterative Schemes: Neural Network Architectures for Operator Learning, Jacobian-Free Backpropagation for Unfolded Schemes with Convergence Guarantees, and Operator Learning Meets Inverse Problems: A Probabilistic Perspective

    • Elementary Real Analysis

      • 1st Edition
      • October 6, 2025
      • Thomas Bieske
      • English
      • Paperback
        9 7 8 0 4 4 3 3 6 7 4 7 2
      • eBook
        9 7 8 0 4 4 3 3 6 7 4 8 9
      Elementary Real Analysis: A Practical Introduction provides a robust foundation for success in real analysis, presenting traditional material in an accessible, engaging manner with the support of clearly outlined learning objectives and exercises.Organized into two well-designed sections, the book begins with a comprehensive review of prerequisite knowledge. Section I includes chapters such as “Sets,” “Properties of Real Numbers,” “Properties of Integers,” and “Functions and Relations,” each accompanied by a wealth of exercises that encourage exploration and practice. These chapters lay the foundation for the second section which delves into advanced topics such as sequences, continuity, and differentiation, culminating in a synthesis of concepts that prepares students for further study of mathematical analysis. For easy reference, two appendices entitled “Mathematical Statements” and “Proof Methods” provide the reader with an accessible reference to the essential language and techniques of proof writing.Whether used in a classroom or for self-directed learning, Elementary Real Analysis: A Practical Introduction is a vital companion for students seeking an introduction to real analysis, bridging the gap between basic principles and advanced mathematical concepts with clarity and precision.

    • An Introduction to Stochastic Modeling

      • 5th Edition
      • September 17, 2025
      • Gabriel Lord + 1 more
      • English
      An Introduction to Stochastic Modeling, Fifth Edition bridges the gap between basic probability and an intermediate level course in stochastic processes, serving as the foundation for either a one-semester or two-semester course in stochastic processes for students familiar with elementary probability theory and calculus. The objectives are to introduce students to the standard concepts and methods of stochastic modeling, to illustrate the rich diversity of applications of stochastic processes in the applied sciences, and to provide an integrated treatment of theory, applications and practical implementation. A well-regarded resource for many years, the text is an ideal foundation for a broad range of students.

    • Recent Developments in Theory and Applications of Fractional Order Systems

      • 1st Edition
      • August 11, 2025
      • Mehmet Yavuz + 2 more
      • English
      • Paperback
        9 7 8 0 4 4 3 2 3 9 5 2 6
      • eBook
        9 7 8 0 4 4 3 2 3 9 5 3 3
      Recent Developments in Theory and Applications of Fractional Order Systems presents a rigorous and thorough analysis of various aspects of Fractional Calculus. The book provides readers with a thorough understanding of fundamental concepts and methods of applied mathematics utilized in a variety of scientific and engineering disciplines. The authors present each computational modeling concept with a definition, methods, theorems, and observations followed by typical application problems and step-by-step solutions. Each topic is covered in detail, followed typically by several meticulously worked out examples and a problem set containing many additional related problems.In addition, the book discusses recent developments and the latest research on Fractional Calculus and its applications, demonstrating important applications in Engineering, Computer Science, Management, Social Science, and the Humanities.

    • Statistics in Industry and Government

      • 1st Edition
      • Volume 53
      • August 6, 2025
      • English
      • Hardback
        9 7 8 0 4 4 3 3 1 4 2 2 3
      • eBook
        9 7 8 0 4 4 3 3 1 4 2 3 0
      Statistics in Industry and Government covers industrial quality control and high-class quality maintenance in products. The book aims to cover as many applications that use statistics as an underlying tool in bringing the best quality products and industrial designs. Chapters in this new release include Analysis of Official Time Series with Ecce Signum, an R Package for Multivariate Signal Extraction and Forecasting, The Maturity Structure of Public Debt: A Granular Approach Using Indian Data, Harnessing the power of spherical intersection: A less arbitrary unsupervised learning method applied to pattern recognition within financial data, and much more.Other chapters in this release include The Use of Causal Inference with Structural Models in Industry, MSME Statistics in India, The Importance of Accurate, Timely, Credible Crime Data to Inform Crime and Justice Policy, Combining Information from Multiple Sources in Official Statistics, Active Learning of Computer Experiment with both Quantitative and Qualitative Inputs, On the use of machine learning methods for missing data problems, Optimal Experimental Planning for Experiments Based on Coherent Systems with Industrial Applications, and more.

    • Methods of Mathematical Modeling

      • 1st Edition
      • July 18, 2025
      • Hemen Dutta
      • English
      • Paperback
        9 7 8 0 4 4 3 1 3 3 1 7 6
      • eBook
        9 7 8 0 4 4 3 1 3 3 1 6 9
      Methods of Mathematical Modeling: Advances and Applications delves into recent progress in this field, highlighting innovative methods and their uses in different domains. This book covers convergence analysis involving nonlinear integral equations and boundary value problems, Navier-Stokes equations in Sobolev-Gevrey spaces, magneto-hydrodynamic... of ternary nanofluids with heat transfer effects, vortex nerve complexes in video frame shape approximation, hybrid schemes for computing hyperbolic conservation laws, and solutions to new fractional differential equations. Additionally, the book examines dynamics of Leslie-Gower type predator-prey models and models for the dynamics of generic crop and water availability.Readers will find diverse approaches, techniques, and applications needed for modeling various physical and natural systems. Each chapter is self-contained, encouraging independent study and application of the modeling examples to individual research projects. This book serves as a valuable resource for researchers, students, educators, scientists, and practitioners involved in different aspects of modeling.

Related subjects

Mathematics and applied mathematics

Statistics and probability