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Introduction to numerical programming: a practical guide for scientists and engineers using Python and C/C++

By: Contributor(s): Series: Series in computational physicsPublication details: CRC Press 2015 Boca RatonDescription: xix, 653 p.: ill. Includes indexISBN:
  • 9780367372866
Subject(s): DDC classification:
  • 005.133 B3I6
Summary: Makes Numerical Programming More Accessible to a Wider AudienceBearing in mind the evolution of modern programming, most specifically emergent programming languages that reflect modern practice, Numerical Programming: A Practical Guide for Scientists and Engineers Using Python and C/C++ utilizes the author's many years of practical research and tea. https://www.taylorfrancis.com/books/mono/10.1201/b17384/introduction-numerical-programming-titus-beu
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Item type Current library Item location Collection Shelving location Call number Status Date due Barcode
Books Vikram Sarabhai Library Rack 3-A / Slot 67 (0 Floor, West Wing) Non-fiction General Stacks 005.133 B3I6 (Browse shelf(Opens below)) Available 203995

Table of content

Machine generated contents note:
1.Approximate Numbers
1.1.Sources of Errors in Numerical Calculations
1.2.Absolute and Relative Errors
1.3.Representation of Numbers
1.4.Significant Digits
1.5.Errors of Elementary Operations
References and Suggested Further Reading
2.Basic Programming Techniques
2.1.Programming Concepts
2.2.Functions and Parameters
2.3.Passing Arguments to Python Functions
2.4.Passing Arguments to C/C++ Functions
2.5.Arrays in Python
2.6.Dynamic Array Allocation in C/C++
2.7.Basic Matrix Operations
3.Elements of Scientific Graphics
3.1.The Tkinter Package
3.2.The Canvas Widget
3.3.Simple Tkinter Applications
3.4.Plotting Functions of One Variable
3.5.Graphics Library graphlib.py
3.6.Creating Plots in C++ Using the Library graphlib.py
4.Sorting and Indexing
4.1.Introduction
4.2.Bubble Sort
Contents note continued: 4.3.Insertion Sort
4.4.Quicksort
4.5.Indexing and Ranking
4.6.Implementations in C/C++
4.7.Problems
5.Evaluation of Functions
5.1.Evaluation of Polynomials by Horner's Scheme
5.2.Evaluation of Analytic Functions
5.3.Continued Fractions
5.4.Orthogonal Polynomials
5.5.Spherical Harmonics
-Associated Legendre Functions
5.6.Spherical Bessel Functions
5.7.Implementations in C/C++
5.8.Problems
6.Algebraic and Transcendental Equations
6.1.Root Separation
6.2.Bisection Method
6.3.Method of False Position
6.4.Method of Successive Approximations
6.5.Newton's Method
6.6.Secant Method
6.7.Birge
Vieta Method
6.8.Newton's Method for Systems of Nonlinear Equations
6.9.Implementations in C/C++
6.10.Problems
7.Systems of Linear Equations
7.1.Introduction
Contents note continued: 7.2.Gaussian Elimination with Backward Substitution
7.3.Gauss
Jordan Elimination
7.4.LU Factorization
7.5.Inversion of Triangular Matrices
7.6.Cholesky Factorization
7.7.Tridiagonal Systems of Linear Equations
7.8.Block Tridiagonal Systems of Linear Equations
7.9.Complex Matrix Equations
7.10.Jacobi and Gauss
Seidel Iterative Methods
7.11.Implementations in C/C++
7.12.Problems
8.Eigenvalue Problems
8.1.Introduction
8.2.Diagonalization of Matrices by Similarity Transformations
8.3.Jacobi Method
8.4.Generalized Eigenvalue Problems for Symmetric Matrices
8.5.Implementations in C/C++
8.6.Problems
9.Modeling of Tabulated Functions
9.1.Interpolation and Regression
9.2.Lagrange Interpolation Polynomial
9.3.Neville's Interpolation Method
9.4.Cubic Spline Interpolation
9.5.Linear Regression
Contents note continued: 9.6.Multilinear Regression Models
9.7.Nonlinear Regression: The Levenberg
Marquardt Method
9.8.Implementations in C/C++
9.9.Problems
10.Integration of Functions
10.1.Introduction
10.2.Trapezoidal Rule; A Heuristic Approach
10.3.The Newton
Cotes Quadrature Formulas
10.4.Trapezoidal Rule
10.5.Simpson's Rule
10.6.Adaptive Quadrature Methods
10.7.Romberg's Method
10.8.Improper Integrals: Open Formulas
10.9.Midpoint Rule
10.10.Gaussian Quadratures
10.11.Multidimensional Integration
10.12.Adaptive Multidimensional Integration
10.13.Implementations in C/C++
10.14.Problems
11.Monte Carlo Method
11.1.Introduction
11.2.Integration of Functions
11.3.Importance Sampling
11.4.Multidimensional Integrals
11.5.Generation of Random Numbers
11.6.Implementations in C/C++
11.7.Problems
Contents note continued: References and Suggested Further Reading
12.Ordinary Differential Equations
12.1.Introduction
12.2.Taylor Series Method
12.3.Euler's Method
12.4.Runge
Kutta Methods
12.5.Adaptive Step Size Control
12.6.Methods for Second-Order ODEs
12.7.Numerov's Method
12.8.Shooting Methods for Two-Point Problems
12.9.Finite-Difference Methods for Linear Two-Point Problems
12.10.Implementations in C/C++
12.11.Problems
13.Partial Differential Equations
13.1.Introduction
13.2.Boundary-Value Problems for Elliptic Differential Equations
13.3.Initial-Value Problems for Parabolic Differential Equations
13.4.Time-Dependent Schrodinger Equation
13.5.Initial-Value Problems for Hyperbolic Differential Equations
13.6.Implementations in C/C++
13.7.Problems
References and Suggested Further Reading



Makes Numerical Programming More Accessible to a Wider AudienceBearing in mind the evolution of modern programming, most specifically emergent programming languages that reflect modern practice, Numerical Programming: A Practical Guide for Scientists and Engineers Using Python and C/C++ utilizes the author's many years of practical research and tea.

https://www.taylorfrancis.com/books/mono/10.1201/b17384/introduction-numerical-programming-titus-beu

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