Yes, it is a 1 x 6 row vector or you can take the transpose and consider it a 6 x 1 column vector. Doesn't really matter. Same for the other matrix.
I know there was a matrix with Keanu Reeves in it. Like that one actually. I also have done few problems with the matrices in math. I don't know...
Say I have two matrices of the form
A = [1 0 0 1 0 1] (1 x 6 row vector)
and
B = [ a b c] (1 x 3 row vector). The number of elements in B = number of 1s in A.
Is there any matrix operation that could be done on A and B that would give me C = [a 0 0 b 0 c]? That is the 1's of A should be...
Sorry I got stuck with course work and other stuff.
My matrix is a square matrix but non-Hermitian and has complex values. Thus non-symmetric. The minimum one I am using is a 81x81 matrix. I have attached the MATLAB matrix variable. For bigger matrices, you can think of repeating this matrix...
Can you please elaborate how to do this? Say I have a 10 x 10 matrix A (which has numerical values) and a 10 x 1 eigen vector x that I am trying to find. Taking Ax = b, I will have 10 equations. How do I solve for x without taking the inverse in matlab? Sorry, but I haven't really done it any...
Thanks a lot for the suggestions!
I have tried parallelizing the code and found it was not helpful. The overhead of splitting the matrices to different workers is much much higher than the actual calculations.
I also tried the inverse power iteration. Works reasonably well for small matrices...
Thanks for the suggestions. Will check it out.
I am trying to find a steady state solution (i.e the eigen vector with eigen value 0 of the matrix). So I am trying to get only one eigen vector. I am using the JDQR algorithm and code from this website...
Hi,
Thanks a lot for your replies! I am using sparse matrices and iterative algorithms to find the eigen value. But I run into issues with regards to memory and also the convergence time for the algorithm, when the matrix becomes too large.
So I was wondering if anyone used specialized stuff...
Hi,
Is anyone here working with (sparse) matrices of size million by million? If so, I would like to know what software you use and any special techniques employed.
PS: I am currently working a project where I need to find eigen value of huge matrices. The best I have been able to do so far...
Refer http://en.wikipedia.org/wiki/Debye_model
There is a formula in there, that connects all the given parameters with the sound velocity. You can also follow the derivation.
Of course to understand more, you might have to refer to some book such as Kittel.
On top of my head, I think this is not a practical solution wit h regards to resolution. The wave length of sound is very large compared to light (At 20 KHz, wavelength of sound is around 1 cm). So one bit in the HDD has to be atleast 1 cm away from the next one for resolution. So if you have...
You need to check the two fluid model for superfluids. Here is one link I got from Google
http://www.yutopian.com/Yuan/TFM.html
Normal fluid represents the non-superfluid part. Nothing to do with vectors.
He-II is a BEC because all of the He atom are in the ground state and can be described...
You should do matrix multiplication(keeping in mind the order) not addition.
The matrices represent operations (hence operators) done on the state. So if we take the input state as |in>, have state after the above circuit is (H x I)|in>, which is then the input to CNOT gate. So finally you get...
Photons are usually entangled using SPDC (Spontaneous Parametric Down conversion), in which basically a photon of higher energy is split into 2 photons of lower energies using a non-linear crystal. The photons are entangled under certain conditions. You can check wikipedia about this.
There...
I came across this today. This work supposedly "reduced the number of theories for HTC from dozens to few"
http://www.physique.usherbrooke.ca/taillefer/publication/Nature-463-519.pdf