LAPACK Users' Guide, 3rd Edition

Applied Numerical Computing

Prof. L. Vandenberghe's lecture note is on applied numerical computing but brings out the practical application aspect. The text covers most areas of numerical linear algebra, nonlinear optimization nonlinear least squares, introduction to floating-point numbers, and rounding errors that are to be needed for understanding the issues of numerical precision. Examples are drawn from signal and image processing, control systems, and machine learning, among other areas, to indicate how these numerical methods are actually applied. This resource aims to fill the gap between theory and practice by providing a practical method for solving computational problems.

Author(s): Prof. L. Vandenberghe

NA Pages

Numerical Computation for Mechanical Engineers

This note introduces elementary programming concepts including variable types, data structures, and flow control. After an introduction to linear algebra and probability, it covers numerical methods relevant to mechanical engineering, including approximation, integration, solution of linear and nonlinear equations, ordinary differential equations, and deterministic and probabilistic approaches.

Author(s): Prof. Anthony T. Patera, Prof. Daniel Frey and Prof. Nicholas Hadjiconstantinou

NA Pages

Numerical Methods for Computational Science and Engineering

The resource described here is an overview of numerical methods important in the study of computational science and engineering. The text starts off with Computing with Matrices and Vectors, foundational elements in many algorithms. The note moves forward and explains Direct Methods for Linear Systems of Equations and Direct Methods for Linear Least Squares Problems, important problem-solving aspects in linear algebra. The Filtering Algorithms for data processing are reviewed, while Data Interpolation and Data Fitting in 1D discuss ways of approximating onedimensional data. Approximation of Functions in 1D and Numerical Quadrature introduce the techniques on function approximation and integration. It also discusses Iterative Methods for Non-Linear Systems of Equations and Eigenvalues-a critical topic needed for solving complex systems. It finally includes Numerical Integration and Structure Preserving Integration, fundamental to perform numerical calculations with appropriate accuracy in scientific computing.

Author(s): Prof. R. Hiptmair

839 Pages

Numerical Methods for Scientific Computing

It gives an explanation of all the different numerical methods of scientific computing. It starts with the basics, which is Root Finding and Orthogonal Functions, solving equations and analyzing functions. Finite Differences and Divided Differences included for the needs in the process of numerical differentiation and interpolation. Interpolation and Curve Fitting are given to outline estimation and modeling. It also includes Z-Transforms and Summation Formulas for signal processing and numerical summation. Quadrature Formulas and Ordinary Differential Equations are explained for integration and the solution of differential equations. Partial Differential Equations, Integral Equations, and Stability and Error Analysis form the advanced topics for numerical methods coverage. Further, Monte Carlo Techniques, Message Passing Interface, and Simulation Modeling are included to point out methods for probabilistic simulations and parallel computing.

Author(s): John Henry Heinbockel

NA Pages