Applied numerical methods with Python for engineers and scientists /
Chapra, Steven C.,
Applied numerical methods with Python for engineers and scientists / Steven C. Chapra, David E. Clough - First edition. - xiii, 652 pages : Illustrations ; 28 cm.
Includes bibliographical references and index.
Table of Contents. - Ch. 1 Mathematical modeling, Numerical methods, and problem solving. - Ch. 2 Python fundamentals. - Ch. 3 Programming in Python. - Ch. 4 Roundoff and Truncation errors. - Ch. 5 Roots: Bracketing methods. - Ch. 6 Roots: Open methods. - Ch. 7 Optimization. - Ch. 8 Linear algebraic equations and matrices. - Ch. 9 Gauss elimination. - Ch. 10 LU Factorization. - Ch. 11 Matrix inverse and condition. - Ch. 12 Iterative methods. - Ch. 13 Eigenvalues. - Ch. 14 Straight-line linear regression. - Ch. 15 General linear and nonlinear regression. - Ch. 16 Fourier analysis. - Ch. 17 Polynomial interpolation. - Ch. 18 Splines and piecewise interpolation. - Ch. 19 Numerical integration formulas. - Ch. 20 Numerical integration of functions. - Ch. 21 Numerical differentiation. - Ch. 22 Initial-Value problems. - Ch. 23 Adaptive methods and stiff systems. - Ch. 24 Boundary-value problems.
"When we first learned to use computers as students in the 1960s, Fortran was the language of choice for most engineering and scientific computations. Over the ensuing half century, numerous other languages have proven useful for implementing the numerical calculations that are so valuable to our research and teaching. Along with a succession of improved Fortran versions, other languages such as Algol, Basic, Pascal, and C/C++ have all found their way into our computational toolbox. The basic content, organization, and pedagogy of this book is like our other numerical methods textbooks. In particular, a conversational writing style is intentionally maintained in order to make the book easier to read. This book tries to speak directly to the reader and is designed in part to be a tool for self-teaching. As such, we also believe it will have value outside the classroom for professionals desiring to gain proficiency in both numerical methods and Python"--
Ages 18+ McGrawHill Education Grades 10-12 McGrawHill Education
9781265017965
1265017964
Engineering--Data processing.
Science--Data processing.
Python (Computer program language)
Numerical analysis.
TA335 / .C53 2022
518.02855133 / CHA
Applied numerical methods with Python for engineers and scientists / Steven C. Chapra, David E. Clough - First edition. - xiii, 652 pages : Illustrations ; 28 cm.
Includes bibliographical references and index.
Table of Contents. - Ch. 1 Mathematical modeling, Numerical methods, and problem solving. - Ch. 2 Python fundamentals. - Ch. 3 Programming in Python. - Ch. 4 Roundoff and Truncation errors. - Ch. 5 Roots: Bracketing methods. - Ch. 6 Roots: Open methods. - Ch. 7 Optimization. - Ch. 8 Linear algebraic equations and matrices. - Ch. 9 Gauss elimination. - Ch. 10 LU Factorization. - Ch. 11 Matrix inverse and condition. - Ch. 12 Iterative methods. - Ch. 13 Eigenvalues. - Ch. 14 Straight-line linear regression. - Ch. 15 General linear and nonlinear regression. - Ch. 16 Fourier analysis. - Ch. 17 Polynomial interpolation. - Ch. 18 Splines and piecewise interpolation. - Ch. 19 Numerical integration formulas. - Ch. 20 Numerical integration of functions. - Ch. 21 Numerical differentiation. - Ch. 22 Initial-Value problems. - Ch. 23 Adaptive methods and stiff systems. - Ch. 24 Boundary-value problems.
"When we first learned to use computers as students in the 1960s, Fortran was the language of choice for most engineering and scientific computations. Over the ensuing half century, numerous other languages have proven useful for implementing the numerical calculations that are so valuable to our research and teaching. Along with a succession of improved Fortran versions, other languages such as Algol, Basic, Pascal, and C/C++ have all found their way into our computational toolbox. The basic content, organization, and pedagogy of this book is like our other numerical methods textbooks. In particular, a conversational writing style is intentionally maintained in order to make the book easier to read. This book tries to speak directly to the reader and is designed in part to be a tool for self-teaching. As such, we also believe it will have value outside the classroom for professionals desiring to gain proficiency in both numerical methods and Python"--
Ages 18+ McGrawHill Education Grades 10-12 McGrawHill Education
9781265017965
1265017964
Engineering--Data processing.
Science--Data processing.
Python (Computer program language)
Numerical analysis.
TA335 / .C53 2022
518.02855133 / CHA