Applied Linear Algebra: QR & Householder
Рет қаралды 12,537
3 жыл бұрынfaculty.washington.edu/kutz/a...
This lecture focuses builds on the classic QR decomposition and introduces the concept of Householder reflectors, allowing for a modified version of Householder triangularization for QR.
@graphicsRat 3 ай бұрын
What an amazing teacher! Thank you for putting these videos online.
@thomasjefferson6225 11 ай бұрын
Gotta admit im impressed with the content this university puts out. Phenomenal this is free.
@syedimtiazalishah6301 3 жыл бұрын
This comment is to appreciate and encourage you to continue making videos like this.
@HassanKhan-cs8ho 3 жыл бұрын
For me; this is the most elegant explanation of QR & Householder
@raba2d723 2 жыл бұрын
agree. watched several videos on this, first one that clicked
@robinamar6454 2 жыл бұрын
Very dynamic and practical teaching :) Thanks Prof Kutz!
@davidloris6307 3 жыл бұрын
great video. very clear explanation with great examples. spent a lot of time lost before i found this
@rubenponsaers9124 3 жыл бұрын
From Belgium here, very good explanation! Keep it up
@anselemokeke8315 2 жыл бұрын
OMG! what an amazing video. Your explanation for this concept is top-notch
@RuanRuankefeng 8 ай бұрын
very clear teachings, thx for professor
@fopbaba 2 жыл бұрын
This is very helpful. Thank you, Prof Kutz
@pmz558 11 ай бұрын
This was very very helpful! Thank you, Prof Kutz
@michaelherediaperez5490 3 ай бұрын
Here is a list of the whole series of videos on Applied Linear Algebra: kzfaq.info/sun/PLFB8R5rtkrDrcuIyA1vKAr9F1s-aETOJ3&si=dXl6UYEGpti-l1CE
@hoangnhatpham8076 3 жыл бұрын
Thanks for the explanation.
@ms2345y 3 жыл бұрын
thank you !
@user-fq3sz6is4s Жыл бұрын
thanks you alot :)
@petersagitarius4356 2 жыл бұрын
Thank you very much... But I have one question. If I use algorithm, which is in video at 42:21 , algorithm works perfect for squared matrix A. If I used matrix A as A = [1 2 3; 4 5 6] (which is rectangular matrix, with less rows than columns) than algorithm crush. If I use traditional Matlab function [Q, R] = qr(A) , it gives correct solution. Please can you somebody explain why, or make a correction of algorithm ? Thank you
@khuyenvuong8771 2 жыл бұрын
Do you show me how to find the documents about topic of this video? I thanks a lot and this is a great video.
@TheRojo387 Жыл бұрын
Like a true mentat, he drinks coffee while lecturing.
@holyshit922 2 жыл бұрын
If I have to write the code for QR decomposition I really will not want to multiply by H matrices This explanation is insufficient for programmers who write the codes for numerical linear algebra
@moshiurrahman9677 2 жыл бұрын
Can you please give me link of any such video? Thanks
@holyshit922 2 жыл бұрын
@@hugginb5929 but if you are capable to write your own pseudo code you dont need to watch this video There is no need for multiplication by H matrix For matrices of sizes l x m and m x n standard multiplication takes O(l*m*n) and O(m x n) extra space but multiplication by H matrices can be done cheaper for both time and space complexity He didn't show effective way to multiply by H matrices so this video is useless and waste of time so anyone who want to code QR will waste 46 min of his life if he watch this video
@holyshit922 2 жыл бұрын
@UCNbJkcasubFH_C4cZLDCSlQ In book written in my native language reduction to Hessenberg form by Gaussian elimination is well described and i had no problems to write code for it I multiplied matrix by some rotation matrices from both left and right and also i was able to write code for QR using rotations While calculation approximated value of eigenvalues i met following problems 1. How to choose shift well 2. Slow convergence for repeated roots 3. How to use deflation 4. Stop crititerion other than maximum number of iterations