You are not logged in.
I found this wikipedia page http://en.wikipedia.org/wiki/Discrete_Fourier_transform that gives a really good and correct definitions and properties of the DFT. It also gives the eigenvalues but doesn't tell exactly how to find them. This is a quote from the page:
Consider the unitary form
defined above for the DFT of length , where . This matrix satisfies the equation:What is not clear form me is why is the characteristic equation
. How can I derive this algebraically? Also, how do I calculate the multiplicity of these eigenvalues? The calculation of the multiplicities is given in this paper "J. H. McClellan and T. W. Parks (1972). "Eigenvalues and eigenvectors of the discrete Fourier transformation". IEEE Trans. Audio Electroacoust. 20 (1): 6674" (http://ieeexplore.ieee.org/search/wrapper.jsp?arnumber=1162342), but my university doesn't have access to this paper, so I haven't read this yet.In summary my questions are as follows:
1) How to derive algebraically the characteristic equation
Thanks in advanced!!!
Can somebody tell me what is the process to calculate the eigenvalues of the discrete fourier transform matrix? Or a reference would be good also. thanks!!
The way to complete the proof is as follows.
Adding [1] and [2] should give you what you are looking for.
Thanks:D
You are right!!
And what about this one:
Thanks a lot!!
Group is simply a touple <A,*> where A is any set, and * is a binary operator with the following properties:
1) Asociative: for any a,b,c in A you have that (a*b)*c)=a*(b*c)
2) Neutral element: there is an element u in A where for any a in A you have that u*a=a
3) Opposite: for any element a in A there is another element called b such that b=-a
About the neighborhood I am not sure to what kind of neighbourhood you are referring to. For example in mathematical programming, a neighbourhood is simply a set of candidate solutions related to another candidate solution by means of a function called neighbourhood structure; this way defining the topology of the candidate solutions space, given an objective function.
Thanks, know is cristal clear!! :-)
Here is a little proof:
A convex set is a set X included in a vector space E where [a,b]={(1-t)a+tb : 0<=t<=1} and a,b in X implies [a,b] in X
Does anyone read the book "Algorithms" by Papadimitriou et al.? I found it to be an excellent book, just sometime need a little bit of explanation in some parts of it. For example, in chapter 2 about divide and conquer algorithms it presents a master theorem about the time complexity for this kind of algoritms. It saids something like this:
"If T(n)=aT(n/b)+O(n^d) for some constants a>0, b>1 and d>=0 then
T(n)= O(n^d) if d>log_b(a)
T(n)= O(n^d log n) if d=log_b(a)
T(n)= O(n^{log_b(a)}) if d<log_b(a)
Where n is the size of the input, a is the branching factor and b is how much we partition the input in each recursion step."
Can anyone tell me what is the parameter d?
Thanks
Hi, I'm new to this forum and I found it very interesting. So, as a first post I present this problem.
"Prove that the set X={(x,y) : ax+by <= c} is convex for any given a, b, and c."
How can I do that using only the axioms of vector spaces?
Thanks