Draft timetable (subject to change) | ||||
---|---|---|---|---|
Week | Lecture videos released | Suggested practical exercises | Thursday 10-10:30am | Thursday 10:30-11am |
Wk 1 | Lecture 1, Lecture 2, Lecture 3 | Practical sheets 0 and 1 | Live Q&A
(Demo: Sheet 1, question 9) (Discussion: C++ advantages) |
|
Wk 2 | Lecture 4, Lecture 5, Lecture 6 | Practical sheet 2 | Live Q&A | Office hours |
Wk 3 | Lec 6A (on sharing), Lecture 7, Lecture 8, Lecture 9 | Practical sheet 3 | Live Q&A | Office hours |
Wk 4 | Lecture 10, Lecture 11, Lec 11A (debugging), Lecture 12, Lecture 13 | Practical sheet 4 | Live Q&A | Office hours |
Wk 5 | - | "More practicals" | Live Q&A / Office hours | |
Wk 6 | - | "More practicals" | Live Q&A / Office hours |
Producing almost any numerical software requires writing codes that manipulate matrices and vectors, making Matlab a natural choice as an introductory programming language for scientific computing. However, the ease of programming in Matlab comes at a cost: the codes take a relatively long time to execute; and the software is commercial. While the use of procedural languages such as Fortran and C will overcome these limitations, they do not allow the straightforward coding of operations between matrices and vectors permitted by Matlab. An alternative approach is to use an object-oriented language such as C++ , where vectors and matrices can be represented as classes. Writing subroutines for these classes that have the same syntax as employed by Matlab allows code developed in Matlab to be translated with minimal effort into C++ code. Such an approach combines the ease of development in the Matlab environment without the associated drawbacks.