Відео

nou joe aur eej ferrie choet tietcher! tenk joe ferrie meutch!!

give examples to explain the topic. You're just reading the slides. Didn't understand a single thing. Same shit is written in my notes, but how do I actually implement it?

Still helping students in 2024, here from Romania, keep up with these kind of videos!

This is insanely good

Hi sir, could you give me any link for the scientific computation course which discusses about advanced linear system solvers? Thank you.

You're always apologizing for the proofs, but they're the best part!

Very clear explanation. This is good stuff. I have 2 questions: 1) Why does Ai stay upper Hessenberg? 2) I don't really grasp why the diagonal elements of Ai converge to the eigenvalues? Thanks for the video!

Reduction interval [a;b] to the [-1;1] interval can be done by substitution but the biggest problem with Gauss-Legendre quadratures is calculation of nodes I would like to see comparison between Gauss-Chebyshev quadratures Chebyshev nodes are easy to compute while Legendre nodes needs numerical methods (I can get coefficients of Legendre polynomial of degree n in linear time from power series solution of differential equation but to calculate nodes I need numerical methods such as QR method for eigenvalues)

Could we use deflation as well to compute the eigenvalues ?

Great content Professor

You've become my professor of linear algebra. Thank you very much.

Super. Thanks.

you explain everything in an extremely clear manner, thanks a lot

Hi Sir this video is exceptional it summarizes all of SVD. I have just a one question can we find AA^T first which will give U matrix and then funding V matrix using relation U(sigma) = AV, i mean the reverse process of what is usually found in books

Thanks! What do you mean by "power of matrices property"?

Great video. Where do you explain the power of matrices property?

how to choose sigmas when shifting not knowing the eig values

Hi. I am Chong Li from Bloodsport. Love your lectures sir!

Wow!

Thank you, dear sir. Very simple and easy to understand. I was a math undergrad, and I am going back for my master's after 10 years, so this is a huge help for revision.

This has been a perfect explanation about the householder's QR decomposition! Really grateful for your video!!

please, can you send me a link to find the gradient(derivative) of norms? thank you

Thank you. Extremely helpful!

Greatly help with my understanding in concepts and algorithms!!

this is so great! Thank's

Wonderful lecture. Missing such mathematics for a long time. Thanks!

i really like your videos but can you explain how to get H2 and H1 simply

Each video of these series is great, thanks from the bottoms of my heart,

Hi Prof Martijn, I always review your videos for Numerical Linear algebra, your explanation is just make sense and intuitive. Thank you.

Hello professor, may I know which book you have taken for reference, thank you 😊

Watching from Zürich, this is so great! You should have more subscribers

you are a bless Dr Martijn. Deep appreciation for your videos, you are a great lecturer

I absolutely loved your explanation for choosing the sign at 23:25, I couldn't find anywhere else on the internet whether we are supposed to use +, - or signum and why, thank you a lot professor.

Excellent explanation!

σ is the smallest singular value of A∗A, µi are the singular values of A, and we have used the fact that A∗A is normal. Why norm of (A*A)^-1=(norm A^+)^2 Can you explain for me ? Thank you so much

I've seen these videos and frankly they are some of the best on the web, in terms of clarity and information. Love it.

Check Video 2-3 in this series. It discusses sensitivity of linear systems

Can you help me explain sensitivity of variable A

One important thing : Cross product is a vector Americans misuse the terms cross product and its length For example in Convex hull they use term cross product no matter that cross product works only for 3D vectors and they have the length of cross product of their mind

Thank you so much for these lectures ❤

can you provide code for this

Dear Professor, thank you very much for the explanation! How could I deal with complex matrices? Can I use QR/Schur for the complex case? As soon as I understood, you derived the explanation for the real values.

And suppose I would like to get some orthogonal polynomials via orthogonalisation How Householder transformation would help ? I know how to use modified Gram-Schmidt for this purpose We have inner product than v_{j}^{T}v_{k} For example inner product for Chebyshov (sh is hard and over that e there are two dots which Russians usually dont write so it is better to write o here) polynomial is sum(a_{2k}*1/(2^(2k)*binomial(2k,k),k=0..floor((m+n)/2)) where p(x)q(x) = sum(a_{k}*x^k,k=0..m+n) so this inner product is different from that produced by v_{j}^{T}v_{k}

well done appreciate your effort

This series has been awesome. Thank you so much for publishing these lectures!

Good stuff-the Dutch was a little confusing but luckily math is universal

Thank you!❤

Absolutely the best explanation -- thank you tremendously!

Thank you professor

Thank you so much sir