Numerical Methods Using Kotlin: For Data Science, Analysis, and Engineering

by Haksun Li PhD

This in-depth guide covers a wide range of topics, including chapters on linear algebra, root finding, curve fitting, differentiation and integration, solving differential equations, random numbers and simulation, a whole suite of unconstrained and constrained optimization algorithms, statistics, regression and time series analysis. The mathematical concepts behind the algorithms are clearly explained, with plenty of code examples and illustrations to help even beginners get started.
In this book, you'll implement numerical algorithms in Kotlin using NM Dev, an object-oriented and high-performance programming library for applied and industrial mathematics. Discover how Kotlin has many advantages over Java in its speed, and in some cases, ease of use. In this book, you’ll see how it can help you easily create solutions for your complex engineering and data science problems.
After reading this book, you'll come away with the knowledge to create your own numerical models and algorithms using the Kotlin programming language.
What You Will Learn Who This Book Is For
Programmers, data scientists, and analysts with prior experience programming in any language, especially Kotlin or Java.

921 pages, Paperback

Published January 1, 2023

Reviews

[Mr. H · Rating 3 out of 5]

A good mathematical reference, though it's main goal is to promote the S2 environment and library, which would've been fine if it worked. S2 is unreachable !