Scientific Computing
Course
Lab
Bold: gradedItalic: optional
Assignments Part I
- Getting startedExercises 2.6, 2.2
- Exercise 2.12 Randomness of bits Diehard testing χ2-testing (2.5.1)
- Exercise 2.9
Exercises 2.7, 2.8 - Exercises 3.4, 3.8
- Volume unit sphere
Assignments Part II
- Exercise 4.2, 4.3
- Exercises 4.4, 4.5 & 4.1
- Exercise 4.6, Applying Genetic Algorithms
- Exercises 4.8, 4.11
- 2. Function minimisation
- Exercises 4.9, 4.10
- Example GA problems
Contact
Education
Master Course 'Laboratory Class Scientific Computing'
Latest news
Lecture notes (new print) are available from the Book store of the student association A-Eskwadraat (Buys Ballot Building, r.238), openingshours: 11:30-13:30.
Instructions on how to include the C++ library on ubuntu.
An adapted Makefile might be required on Ubuntu or Mac.
This page is the home of the 2014–2015 installment of the Laboratory
Class Scientific Computing.
Overview
Participation
Please register through Osiris Online. Course code: WISM454.
Format
| Instructor | : | Gerard Sleijpen, Room FG k504, E-mail: G.L.G.Sleijpen@uu.nl |
| Tristan van Leeuwen, Room FG k609, E-mail: T.vanLeeuwen@uu.nl | ||
| Schedule | : | Weekly computer sessions, in periods 1 and 2 (wk. 37 [2014] – 51 [2014]). |
| Mondays 9:00–13:00. First session Monday, September 8, 2014 | ||
| Note:The discussion on Monday, September 8, 2014 (9:00-9:15) did not lead to another timeslot: the lectures will be on Mondays from 9:00-12:45 (as announced in OSIRIS). Due to the large number of participants, we might move to another Lecture Room (to be announced at this site). | ||
| Location | : | Freudenthal Gebouw 514 (old name Wiskunde Gebouw, Math. Building). |
| Credits | : | 7.5 ECTS |
| Grading | : | Grade = ((average assignment grade) + 4 x (average report grade))/5 |
Literature
We use lecture notes, available from the Beta student desk on the first floor of the Buys-Ballot building.
Topics
The aim of the course is to learn about a number of important scientific computing subjects, and to meet with the various aspects of scientific computing. You will (partially) write your own computer code and run simulations, handle output data and visualise it. The theory and results of the simulations are to be presented by means of written reports.
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Random number generation and numerical integration using Monte Carlo (MC) methods.
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Solving optimisation problems using Genetic Algorithms (GAs).
Contents
This is a hands on course which is intended to expose you to computer techniques used in scientific computation. It covers the full process from the theory to programming (C++), experimenting (MATLAB), and finally to writing a report. In this year's course we will focus our attention on two subjects:
- Monte Carlo integration
- Optimisation by genetic algorithms
We will cover topics such as how to choose a good random number generator and how to apply techniques such as biased sampling and discrepancy sampling. Since Monte Carlo does not suffer from the 'curse of dimensionality' we can fairly efficiently approximate the volume of, say, the d-dimensional unit sphere, for large d.
In genetic algorithms we use a population that represents elements in the search space of an optimisation problem. Techniques such as elitism and Gray codes improve the optimising effects of mutation and crossover. Combined with local search, the resulting hybrid method yields a powerful optimisation program, typically for hard (NP-hard) problems.
Reports
For each of the two main subjects in this course, you will have to present your results in a report written in English and formatted using LaTeX. Look here for deadlines and additional demands. Read the Introduction and Appendix A in the lecture notes for further instructions.