Solving Engineering Problems Using MATLAB C++ Math Library Introduction
In the previous article, we studied how can use MATLAB C API to solve Engineering problems. In this article I will show you how can use MATLAB C++ math library. The MATLAB® C++ Math Library serves two separate constituencies: MATLAB programmers seeking more speed or complete independence from interpreted MATLAB, and C++ programmers who need a fast, easy-to-use matrix math library. To each, it offers distinct advantages.
As all of you know, MATLAB is a powerful Engineering language. Because of some limitation, some tasks take very long time to proceed. Also MATLAB is an interpreter not a compiler. For this reason, executing a MATLAB program (m file) is time consuming. For solving this problem, Mathworks provides us C Math Library or in common language, MATLAB API. A developer can employ these APIs to solve Engineering problems very fast and easy. This article is about how can use these APIs.
This title demonstrates how to develop computer programmes which solve specific Engineering problems using the finite element method. It enables students, scientists and engineers to assemble their own computer programmes to produce numerical results to solve these problems. The first three editions of Programming the Finite Element Method established themselves as an authority in this area. This fully revised 4th edition includes completely rewritten programmes with a unique description and list of parallel versions of programmes in Fortran 90. The Fortran programmes and subroutines described in the text will be made available on the Internet via anonymous ftp, further adding to the value of this title.
When working with mathematical simulations or Engineering problems, it is not unusual to handle curves that contains thousands of points. Usually, displaying all the points is not useful, a number of them will be rendered on the same pixel since the screen precision is finite. Hence, you use a lot of resource for nothing!
This article presents a fast 2D-line approximation algorithm based on the Douglas-Peucker algorithm (see [1]), well-known in the cartography community. It computes a hull, scaled by a tolerance factor, around the curve by choosing a minimum of key points. This algorithm has several advantages:
這是一個基于Douglas-Peucker算法的二維估值算法。
算法導論英文版
This book is one of a series of texts written by faculty of the Electrical Engineering and
Computer Science Department at the Massachusetts Institute of Technology. I
