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 Learn
Program in Kotlin using a high-performance numerical library
Learn the mathematics necessary for a wide range of numerical computing algorithms
Convert ideas and equations into code
Put together algorithms and classes to build your own engineering solutions
Build solvers for industrial optimization problems
Perform data analysis using basic and advanced statistics

Who This Book Is For
Programmers, data scientists, and analysts with prior experience programming in any language, especially Kotlin or Java.