Problems in which an operator or function can be represented with asymptotically less data than the naive representation. Not limited to sparse matrices.
11
votes
5answers
1k views
What is the fastest way to calculate the largest eigenvalue of a general matrix?
EDIT: I am testing if any eigenvalues have a magnitude of one or greater.
I need to find the largest absolute eigenvalue of a large sparse, non-symmetric matrix.
I have been using R's eigen() ...
6
votes
1answer
201 views
Sparse hermitian eigensystems: are there better techniques than Arpack or TRLan?
As a part of other work I need to solve relatively large (~1E5x1E5) and sparse (~100 non-zero elements in each raw in few blocks) hermitian eigensystems. Usually only few eigenvalues+vectors are ...
4
votes
3answers
306 views
Efficient assembly of finite element matrix in MATLAB
Question
What is the most efficient algorithm for finding a row of a matrix which matches a given row? This is the same as a table lookup based on multiple criteria.
Context
Finite Element Matrices ...
7
votes
1answer
129 views
Solving a system with a small rank diagonal update
Suppose I have the original large, sparse linear system: $A\textbf{x}_0=\textbf{b}_0$. Now, I do not have $A^{-1}$ as A is too large to factor or any sort of decomposition of $A$, but assume that I ...
5
votes
2answers
146 views
Is it possible to ignore/discard part of a matrix when finding eigenvalues?
I have have multiple large matrices for which I need to find the largest absolute eigenvalue. I know that there is a large submatrix that does not vary. Is it possible to ignore/discard the submatrix?
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