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Numerical methods using Java = for d...

Li, Haksun.

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Numerical methods using Java = 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 Java/ by Haksun Li, PhD.
其他題名:	for data science, analysis, and engineering /
作者:	Li, Haksun.
出版者:	Berkeley, CA :Apress : : 2022.,
面頁冊數:	xiv, 1186 p. :ill., digital ;24 cm.
內容註:	1: Introduction to Numerical Methods in Java -- 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 -- References.
Contained By:	Springer Nature eBook
標題:	Java (Computer program language) -
電子資源:	https://doi.org/10.1007/978-1-4842-6797-4
ISBN:	9781484267974

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

Numerical methods using Javafor data science, analysis, and engineering /[electronic resource] :by Haksun Li, PhD. - Berkeley, CA :Apress :2022. - xiv, 1186 p. :ill., digital ;24 cm.

1: Introduction to Numerical Methods in Java -- 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 -- References.

Implement numerical algorithms in Java using the NM Dev, an object-oriented and high-performance programming library for mathematics.You'll see how it can help you easily create a solution for your complex engineering problem by quickly putting together classes. Numerical Methods Using Java 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. You will: Program in Java using a high-performance numerical library Learn the mathematics for a wide range of numerical computing algorithms Convert ideas and equations into code Put together algorithms and classes to build your own engineering solution Build solvers for industrial optimization problems Do data analysis using basic and advanced statistics.

ISBN: 9781484267974

Standard No.: 10.1007/978-1-4842-6797-4doiSubjects--Topical Terms:

522522
Java (Computer program language)

LC Class. No.: QA76.73.J38 / L52 2022

Dewey Class. No.: 005.133

Numerical methods using Java = for data science, analysis, and engineering /
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505 0 $a 1: Introduction to Numerical Methods in Java -- 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 -- References.
520 $a Implement numerical algorithms in Java using the NM Dev, an object-oriented and high-performance programming library for mathematics.You'll see how it can help you easily create a solution for your complex engineering problem by quickly putting together classes. Numerical Methods Using Java 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. You will: Program in Java using a high-performance numerical library Learn the mathematics for a wide range of numerical computing algorithms Convert ideas and equations into code Put together algorithms and classes to build your own engineering solution Build solvers for industrial optimization problems Do data analysis using basic and advanced statistics.
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