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

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 LearnProgram in Kotlin using a high-performance numerical libraryLearn the mathematics necessary for a wide range of numerical computing algorithmsConvert ideas and equations into codePut together algorithms and classes to build your own engineering solutionsBuild solvers for industrial optimization problemsPerform data analysis using basic and advanced statisticsWho This Book Is ForProgrammers, data scientists, and analysts with prior experience programming in any language, especially Kotlin or Java.