Applied Mathematics

Compiles top research from APAMCS-2022, covering numerical methods, scientific computing, data analysis, machine learning, modular computing, and mathematics education. Topics include optimization, deep learning, neural networks, and AI. It’s aimed at researchers in computing, machine learning, information security, and math education.

Traces the history of numerical methods for solving linear systems and eigenvalue problems, highlighting their evolution alongside computing tools. It includes 78 biographies of key contributors and appeals to researchers in numerical linear algebra and historians of mathematics alike.

Elementary introduction to problem of cumulative effect of rounding errors in a very large number of arithmetical calculations—particularly applicable to computer operations. Simple representative analyses illustrate techniques. Topics include fundamental arithmetic operations, computations involving polynomials and matrix computations. Results deal exclusively with digital computers but are equally applicable to desk calculators.

A comprehensive book on stereology presents geometric sampling methods to estimate 3D biological and material structures from 2D images. With 20 case studies, it combines theory, methodology, and applications, offering tools for unbiased measurement in medical, biological, and geological contexts—ideal for researchers and postgraduate courses in imaging and quantification.

An annual anthology showcasing the year’s best math writing, exploring its history, philosophy, teaching, and real-world impact. Featuring essays by renowned and emerging voices, it offers accessible insights into topics like cryptocurrencies, dark data, and time, making mathematics engaging for both experts and general readers alike.