Numerical Methods Using Java (PDF)
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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
Additionally, Haksun is the vice dean of the Big Data Finance and Investment Institute of Fudan University, China. He was an adjunct professor with multiple universities. He has taught at the National University of Singapore (mathematics), Nanyang Technological University (business school), Fudan University (economics), as well as Hong Kong University of Science and Technology (mathematics). Dr. Haksun Li has a B.S. and M.S. in pure and financial mathematics from the University of Chicago, and an M.S. and a PhD in computer science and engineering from the University of Michigan, Ann Arbor.
- Autor: Li
- 2022, 1st ed, 1186 Seiten, Englisch
- Verlag: APress
- ISBN-10: 1484267974
- ISBN-13: 9781484267974
- Erscheinungsdatum: 01.01.2022
Abhängig von Bildschirmgrösse und eingestellter Schriftgrösse kann die Seitenzahl auf Ihrem Lesegerät variieren.
- Dateiformat: PDF
- Grösse: 27 MB
- Ohne Kopierschutz
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