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Numerical methods using Kotlin = for...

Li, Haksun.

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Numerical methods using Kotlin = for data science, analysis, and engineering /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Numerical methods using Kotlin/ by Haksun Li, PhD.
其他題名:	for data science, analysis, and engineering /
作者:	Li, Haksun.
出版者:	Berkeley, CA :Apress : : 2023.,
面頁冊數:	xxii, 899 p. :ill., digital ;24 cm.
內容註:	1: Introduction to Numerical Methods in Kotlin -- 2: Linear Algebra -- 3: Finding Roots of Equations -- 4: Finding Roots of Systems of Equations -- 5: Curve Fitting and Interpolation -- 6: Numerical Differentiation and Integration -- 7: Ordinary Differential Equations -- 8: Partial Differential Equations -- 9: Unconstrained Optimization -- 10: Constrained Optimization -- 11: Heuristics -- 12: Basic Statistics -- 13: Random Numbers and Simulation -- 14: Linear Regression -- 15: Time Series Analysis.
Contained By:	Springer Nature eBook
標題:	Kotlin (Computer program language) -
電子資源:	https://doi.org/10.1007/978-1-4842-8826-9
ISBN:	9781484288269

Numerical methods using Kotlin = for data science, analysis, and engineering /
Li, Haksun.

Numerical methods using Kotlinfor data science, analysis, and engineering /[electronic resource] :by Haksun Li, PhD. - Berkeley, CA :Apress :2023. - xxii, 899 p. :ill., digital ;24 cm.

1: Introduction to Numerical Methods in Kotlin -- 2: Linear Algebra -- 3: Finding Roots of Equations -- 4: Finding Roots of Systems of Equations -- 5: Curve Fitting and Interpolation -- 6: Numerical Differentiation and Integration -- 7: Ordinary Differential Equations -- 8: Partial Differential Equations -- 9: Unconstrained Optimization -- 10: Constrained Optimization -- 11: Heuristics -- 12: Basic Statistics -- 13: Random Numbers and Simulation -- 14: Linear Regression -- 15: Time Series Analysis.

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. You will: 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.

ISBN: 9781484288269

Standard No.: 10.1007/978-1-4842-8826-9doiSubjects--Topical Terms:

3619943
Kotlin (Computer program language)

LC Class. No.: QA76.62 / .L52 2023

Dewey Class. No.: 005.133

Numerical methods using Kotlin = for data science, analysis, and engineering /
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505 0 $a 1: Introduction to Numerical Methods in Kotlin -- 2: Linear Algebra -- 3: Finding Roots of Equations -- 4: Finding Roots of Systems of Equations -- 5: Curve Fitting and Interpolation -- 6: Numerical Differentiation and Integration -- 7: Ordinary Differential Equations -- 8: Partial Differential Equations -- 9: Unconstrained Optimization -- 10: Constrained Optimization -- 11: Heuristics -- 12: Basic Statistics -- 13: Random Numbers and Simulation -- 14: Linear Regression -- 15: Time Series Analysis.
520 $a 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. You will: 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.
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