LFD Book Forum

LFD Book Forum (http://book.caltech.edu/bookforum/index.php)
-   Homework 7 (http://book.caltech.edu/bookforum/forumdisplay.php?f=136)
-   -   good tutorials on constrained optimization (http://book.caltech.edu/bookforum/showthread.php?t=4029)

ilya239 02-25-2013 01:28 PM

good tutorials on constrained optimization
 
What are some good online tutorials on the constrained optimization methods used in lecture (Lagrange multipliers for inequality constraints, and quadratic programming)? I want to better understand why an optimal solution to the problem given to the quadratic solver corresponds to a maximum-margin classifier for the original problem. Many thanks!
p.s. Offline (textbook) references are welcome as well.

yaser 02-25-2013 03:54 PM

Re: good tutorials on constrained optimization
 
Quote:

Originally Posted by ilya239 (Post 9530)
What are some good online tutorials on the constrained optimization methods used in lecture (Lagrange multipliers for inequality constraints, and quadratic programming)? I want to better understand why an optimal solution to the problem given to the quadratic solver corresponds to a maximum-margin classifier for the original problem. Many thanks!
p.s. Offline (textbook) references are welcome as well.

Here is a textbook reference for optimization:

http://www.stanford.edu/~boyd/cvxbook/

Maximizing the margin was reduced to a condition on the norm of w, and that was shown to be equivalent to the QP problem in this segment of the lecture:


ilya239 02-26-2013 11:34 AM

Re: good tutorials on constrained optimization
 
Quote:

Originally Posted by yaser (Post 9538)
Here is a textbook reference for optimization:
http://www.stanford.edu/~boyd/cvxbook/

Thanks a lot, this is a good treatment complementary to the ones I've found.


All times are GMT -7. The time now is 04:02 AM.

Powered by vBulletin® Version 3.8.3
Copyright ©2000 - 2019, Jelsoft Enterprises Ltd.
The contents of this forum are to be used ONLY by readers of the Learning From Data book by Yaser S. Abu-Mostafa, Malik Magdon-Ismail, and Hsuan-Tien Lin, and participants in the Learning From Data MOOC by Yaser S. Abu-Mostafa. No part of these contents is to be communicated or made accessible to ANY other person or entity.