Timeline for There must be a good introductory numerical analysis course out there!
Current License: CC BY-SA 3.0
6 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| May 20, 2011 at 16:35 | vote | accept | Nilima Nigam | ||
| May 20, 2011 at 16:35 | |||||
| May 19, 2011 at 15:21 | comment | added | Igor Rivin | Whether the course would be suitable for undergraduates is in the eye of the beholder, but it certainly requires no fancy prerequisites... | |
| May 19, 2011 at 8:57 | comment | added | Suvrit | Sorry, I meant to link to: ee.ucla.edu/~vandenbe | |
| May 19, 2011 at 8:56 | comment | added | Suvrit | Boyd's webpage: stanford.edu/~boyd has links to several courses, where a lot of the material relevant to the book is covered. I would love to take part in a an effort to design a nice undergraduate level course on numerical optimization. Also note, that math undergrads who are happy with Python might then be able to benefit from tools like CVX, CVXOPT, CVXMOD, etc.--which can also be used to tackle many of the exercises in Boyd and Vandenberghe's book. I also like Lieven's course material, e.g. ee.ucla.edu/~vandenbe/ee103.html | |
| May 19, 2011 at 3:51 | comment | added | Nilima Nigam | Indeed, Igor, numerical analysis is a big field! Analogously, even though analysis is gigantic, the first analysis course usually manages to introduce important ideas in an elegant framework, which we all tend to follow. Thanks for the suggestion of Boyd's book. Do you know of a course designed for undergrads based on this book? The preface describes it as aimed at the introductory graduate level. | |
| May 19, 2011 at 2:53 | history | answered | Igor Rivin | CC BY-SA 3.0 |