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ISBN10: 1266651497 | ISBN13: 9781266651496
ISBN10: 1266651497
ISBN13: 9781266651496
* The estimated amount of time this product will be on the market is based on a number of factors, including faculty input to instructional design and the prior revision cycle and updates to academic research-which typically results in a revision cycle ranging from every two to four years for this product. Pricing subject to change at any time.
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Applied Numerical Methods with Python, 1st Edition is written for students who want to learn and apply numerical methods in order to solve problems in engineering and science. As such, the methods are motivated by problems rather than by mathematics. That said, sufficient theory is provided so that students come away with insight into the techniques and their shortcomings. If you’ve ever used Applied Numerical Methods for MATLAB, you’ll find transitioning to this Python program seamless! No prior experience with Python is required.
McGraw Hill Connect, is available with this title. In addition to providing the MHeBook, Connect is the only integrated learning system that empowers students by continuously adapting to deliver precisely what they need, when they need it, how they need it, so that class time is more effective. Connect enables professors to assign homework, quizzes, and tests easily and automatically grades and records the scores of the student's work.
1 Mathematical Modeling, Numerical Methods, and Problem Solving
2 Python Fundamentals
3 Programming in Python
4 Roundoff and Truncation Errors
5 Roots: Bracketing Methods
6 Roots: Open Methods
7 Optimization
8 Linear Algebraic Equations and Matrices
9 Gauss Elimination
10 LU Factorization
11 Matrix Inverse and Condition
12 Iterative Methods
13 Eigenvalues
14 Straight-Line Linear Regression
15 General Linear and Nonlinear Regression
16 Fourier Analysis
17 Polynomial Interpolation
18 Splines and Piecewise Interpolation
19 Numerical Integration Formulas
20 Numerical Integration of Functions
21 Numerical Differentiation
22 Initial-Value Problems
23 Adaptive Methods and Stiff Systems
24 Boundary-Value Problems
2 Python Fundamentals
3 Programming in Python
4 Roundoff and Truncation Errors
5 Roots: Bracketing Methods
6 Roots: Open Methods
7 Optimization
8 Linear Algebraic Equations and Matrices
9 Gauss Elimination
10 LU Factorization
11 Matrix Inverse and Condition
12 Iterative Methods
13 Eigenvalues
14 Straight-Line Linear Regression
15 General Linear and Nonlinear Regression
16 Fourier Analysis
17 Polynomial Interpolation
18 Splines and Piecewise Interpolation
19 Numerical Integration Formulas
20 Numerical Integration of Functions
21 Numerical Differentiation
22 Initial-Value Problems
23 Adaptive Methods and Stiff Systems
24 Boundary-Value Problems
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