Tagged Questions

An eigenvector of an operator is a vector such that the action of the operator is the same as multiplication by a constant, called the eigenvalue. The eigensystem of an operator is the set of all such eigenvectors and their associated eigenvalues.

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2answers
80 views

Eigenvectors: MATLAB vs LAPACK DGGEV or DGGEVX

If we call LAPACK DGGEV or DGGEVX routines for two badly-conditioned matrices in a C++ code, will we get the same Eigen values & vectors of MATLAB eig command ...
1
vote
1answer
44 views

Fortran 2003 ARPACK wrapper

I wrote a Fortran 2003 wrapper for the ARPACK routine znaupd, basically translating the the example driver routine zndrv1 into modern Fortran 2003 language with automatic arrays. I initialize every ...
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2answers
187 views

Most efficient library to diagonalize exactly large hermitian or unitary matrices

I am working on a physics problem which requires obtaining the exact eigenvalues and eigenvectors of Hermitian and Unitary matrices numerically. Naturally I would like to ask the experts what are the ...
2
votes
1answer
173 views

Comparing Eigenvectors, Mathematica vs. Matlab

I am trying to create the same out puts in Mathmatica and Matlab, however I am running into trouble aligning the eigenvectors with the eigenvalues, I think the Matlab is doing something slighly more ...
5
votes
2answers
99 views

Sparse smallest eigenvalue problem on a linear subspace?

I am interested in methods for solving the optimization problem $$ \begin{array}{rl} \arg\min_x & x^T A x \\ \mathrm{s.t.} & x^T x = 1 \\ & Bx = 0 \end{array} $$ where $A$ is symmetric ...
2
votes
1answer
71 views

Finding Interior eigenvalues using Davidson algorithm

Is it possible to find interior eigenvalues closer to some lambda using Davidson method. I was searching online but found that most people use Jacobi-Davidson method for that. Thanks
5
votes
1answer
72 views

Computing eigendecomposition of a Hermitian matrix that is almost unitary

I have a dense Hermitian matrix that is approximately unitary, so it has eigenvalues that are $\sim \pm1$. I would like to compute all the eigenvectors corresponding to the $+1$ eigenvalue (not ...
1
vote
3answers
152 views

How to find the smallest positive eigenvalue of a large general system if they are all in +/- pairs of real eigenvalues

I used MKL Lapack SBGVX because it can solve for "selected" eigenvalues/modes (both positive and negative), thinking it would be efficient, but it is extremely slow when compared with Bathe's subspace ...
2
votes
1answer
51 views

last column of SPD matrix given it's spectral decomposition

I'm working on this application where I get the spectral decomposition (O,D) of a matrix A for free (A is of full rank) but not A itself, and I need to only recover the last column of A. I was ...
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0answers
27 views

eigenvalues of graph $\mathbb{Z}_m \times \mathbb{Z}_n$

I am trying to find the eigenvalues of the Laplacian for the product of two cyclic graphs $\mathbb{Z}_L \times \mathbb{Z}_M$. I took a naive approch and coded the matrix by hand: ...
3
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0answers
34 views

Dominant contributions of a quadratic form

Let $\Sigma$ be a covariance matrix (e.g. symmetric positive definite). For arbitrary vectors $\epsilon$, I need to compute $\chi^2 \equiv \epsilon^\top\Sigma^{-1}\epsilon$, which I do using a ...
1
vote
0answers
69 views

Locally conservative method for differential generalized eigenvalue problem

I have to approximate the smallest eigenvalue of the following generalized eigenvalue problem $$ - \nabla \cdot D(x) \nabla p(x) + \alpha(x) p(x) = \lambda \beta(x) p(x) $$ over a domain like ...
5
votes
2answers
164 views

Is there guaranteed global solver for such an eigenvalue problem?

The original nonlinear optimization problem I have is as follows: For constant symmetric matrices $A=A^T, B_i=B_i^T(\forall i\in\mathbb{N}) \in \mathbb{R}^{n\times n}, \text{rank}(A)=n,$ ...
2
votes
2answers
135 views

Algorithm for directly finding the leading eigenvector of an irreducible matrix

According to the Perron-Frobenius theorem, a real matrix with only positive entries (or one with non-negative entries with a property called irreducibility) will have a unique eigenvector that ...
2
votes
0answers
45 views

Are the eigenvalues of the product matrix of two real symmetric square matrices also real values?

Suppose $A,B \in \mathbb{R}^{n\times n}; A=A^T, B=B^T$, let $C = AB, D =BA$, If we have all the real eigenvalues of $A$ and $B$, e.g. the eigenvalue decomposition of them: $A=P\Lambda_1 P^T$, ...
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0answers
69 views

Sorting eigenvalues by the dominant contribution

[Edited to simplify the question] I am trying to associate the eigenvalues $E$ of a matrix $H$ to the original rows of the matrix. Moreover, it would be trivial to sort the eigenvalues in ascending ...
2
votes
1answer
126 views

Algorithm for Eigenvalue Problem of a Real Symmetric nxn Matrix

I have a nxn covariance matrix (so, real, symmetric, dense, nxn... I mean positive semi-definite). 'n' may be very very very big! I'd like to solve partial (3 largest) eigenvalue (+eigenvectors) ...
3
votes
2answers
190 views

Algorithm for Complete Eigenvalue Problem of a Real Symmetric nxn Matrix

I have a nxn covariance matrix (so, real, symmetric, dense, nxn). 'n' may be very very very big! I'd like to solve complete eigenvalue (+eigenvectors) problem for this matrix. Could somebody tell me ...
8
votes
0answers
218 views

Implementation of Jacobi-Davidson method for cubic eigenvalue problem

I have a large cubic eigenvalue problem: $$\left(\mathbf{A}_0 + \lambda\mathbf{A}_1 + \lambda^2\mathbf{A}_2 + \lambda^3\mathbf{A}_3\right)\mathbf{x} = 0.$$ I could solve this by converting to a ...
2
votes
1answer
122 views

How to implement the spectral decomposition of a symmetric dense matrix via Eigen C++

Spectral decomposition of symmetric matrix $A_{n\times n}$, specifically, $n=3$ find the orthogonal matrix $Q$ and diagonal matrix $\Lambda$ such that: $A=Q\Lambda Q^T$ How to implement such ...
2
votes
2answers
147 views

About Subspace Iteration for Eigenvalues

I heard that subspace iteration plus Ritz acceleration could improve the performance a lot for solving clustered eigenvalues, for the eigenvalues and eigenvectors could converge linearly with ratio ...
6
votes
1answer
513 views

How to use Lanczos method to compute eigenvalues and eigenvectors

I have a sparse and symmetric matrix A(n x n). The method Lanczos tranforms matrix A into tridiagonal and symmetric matrix T and the Lanczos vectors in matrix V. From there how do I compute k ...
3
votes
1answer
86 views

Solver for eigensystem of vectors?

I'm trying to solve a multipole system. It involves a matrix of 3x3 tensors $A_{ij}$ and a vector of 3-tuples $\mathbf v_i$. $$\left(\begin{matrix} A_{11} & A_{12} & \cdots & A_{1n}\\ ...
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votes
0answers
24 views

Size reduction of matrices in dispersion curve calculation

I have an energy dispersion curve resulted from eigen values of E(k) = eig(T exp(ik) + T' exp(-ik) + H0). Where H0 and T are NxN square matrices and T' is transpose of T and k is wave-vector. So there ...
4
votes
1answer
98 views

What is the added cost of generalizing an eigensystem?

Problem Let's say I can write a model as the Hermitian eigensystem: $$ A x = \lambda x $$ where $A \in \mathbb{C}^{n\times n}$ is Hermitian, or as the generalized Hermitian eigensystem: $$ \tilde A ...
2
votes
1answer
71 views

Does Lanczos have trouble with large matrix elements?

I have a large, yet very sparse, matrix that I'd like to diagonalize. Both my own Lanczos implementation and the ARPACK that's built in with scipy fail to converge properly, though. I know that my ...
2
votes
1answer
214 views

how to use lanczos code from netlib for large sparse symmetric matrix?

I want to use lanczos method to calculate the few lowest eigenvalue and eigen-vector of a large sparse symmetric matrix(~50k x ~50k). In http://www.netlib.org/lanczos/index.html I found the codes ...
4
votes
3answers
182 views

Preconditioner for finding the smallest eigenpairs of a large, but structured, matrix

I'm trying to find the eigenvector corresponding to the second smallest eigenvalue of a large $(4,000,000 \times 4,000,000)$ matrix $L$. $L$ is a graph Laplacian, with the following structure: $L = D ...
2
votes
0answers
67 views

Preconditioner for large size hermitian eigenvalue problems

Basically I try to compute several smallest eigenvalues of some sparse 50k*50k eigenvalue problems using matlab. $$Ax = \lambda Bx$$ With matlab eigs, it's not as fast as I expected. So I tried some ...
8
votes
2answers
209 views

Continuity of eigenvectors of parametric matrix

I have $n$-dimensional matrices $\mathrm{\hat{H}}(\vec{k})$ depending on vector parameter $\vec{k}$. Now, eigenvalue routines return eigenvalues in no particular order (they are usually sorted), but ...
1
vote
2answers
155 views

how can I numerically calculate all eigenvectors of a $n \times n$ complex tridiagonal matrix?

I have tried matlab eig command, it results true eigenvalues but wrong eigenvectors. I also tried direct iteration with rayleigh qotient which is better but doesn't give all eigenvectors also I have ...
7
votes
1answer
320 views

Solving a large non-hermitian generalised eigenvalue problem from a linear stability analysis using SLEPc

I have a generalised matrix problem: $A x = \lambda B x$ from a spectral method on a linear stability analysis problem. My matrix B is diagonal and positive semi-definite. A is non-hermitian and ...
5
votes
2answers
222 views

find a set of linearly independent columns in a $m\times n$ matrix

my question is between mathematics, physics and informatics. Suppose i have an Hamiltonian (hermitian matrix) that i can diagonalize. The matrix that allows this transformation is a unitary matrix ...
4
votes
3answers
2k views

Algorithm for Principal Eigenvector of a Real Symmetric 3x3 Matrix

I have a 3x3 covariance matrix (so, real, symmetric, dense, 3x3), I would like it's principal eigenvector, and speed is a concern. Is there a fast algorithm for this specific problem? I've seen ...
8
votes
1answer
1k views

What is the fastest way to compute all eigenvalues of a very big and sparse adjacency matrix in python?

I'm trying to figure out if there is a faster way to compute all the eigenvalues and eigenvectors of a very big and sparse adjacency matrix than using scipy.sparse.linalg.eigsh As far as I know, this ...
5
votes
1answer
598 views

generalized eigenvalue problem

I need to solve a real generalized eigenvalue problem $Ax= \lambda Bx(*)$ A and B are calculated from equations below: $$A=\sum_{i,j=1}^{N}W_{ij}(K_{i}-K_{j})\beta\beta^{T}(K_{i}-K_{j})^{T}$$ ...
1
vote
1answer
109 views

Ground state eigenvector different for different eigen solvers (differs by negative sign in the elements). Does it matter?

Here is some code that hopefully clearly illustrates what I'm doing: ...
5
votes
3answers
469 views

computing the determinant of a dense nonsymmetric 100x100 matrix having very big and very small eigenvalues

The motivation for my question is the following: in one of Project Euler questions there is a need to count the spanning trees of a rectangular grid graph of dimension 100x500. By the Matrix-Tree ...
9
votes
0answers
135 views

Specialized methods for symmetric tridiagonal generalized eigenvalue problems

I have to solve generalized eigenvalue problems $Ax = \lambda Bx$ where $A$ and $B$ are both tridiagonal, $B$ is symmetric positive definite and real, but $A$ is only complex symmetric (not definite ...
4
votes
1answer
162 views

Numerical Methods for minimizing a Non-Differentiable Convex Function of Several Variables

I have a multi-variable convex continuous function which is not differentiable. I am interested to know about different numerical techniques, possibly also references to them, used for this. Read ...
4
votes
1answer
463 views

Simple Lanczos algorithm code to obtain eigenvalues and eigenvectors of a symmetric matrix

I would like to write a simple program (in C) using Lanczos algorithm. I came across a Matlab example which helped me to understand a bit further the algorithm, however from this piece of code I can't ...
7
votes
2answers
371 views

Finding the distribution (histogram) of eigenvalues for large sparse matrices

Are there any existing programs that are able to compute the (approximate) distribution of eigenvalues for very large (symmetric) sparse matrices? Note that I do not need the eigenvalues themselves, ...
4
votes
1answer
288 views

On implementations of the complex QR decomposition

I have an mathematical theorem on the QR decomposition, which relies on the QR decomposition of an invertible square complex matrix always constructing a triangular matrix with real diagonal entries. ...
1
vote
2answers
638 views

Implementing PageRank using the Power Method

I am trying to implement the PageRank algorithm described in this paper (Fig. 1). Here is the breakdown of the steps: where: pT is a probability distribution ...
2
votes
2answers
628 views

An eigenvalue algorithm to solve constrained quadratic form minimization

I have a quadratic form $\mathbf{x}^T A \mathbf{x}$ (where $A\in \mathbb{R}^{n\times n}$ is symmetric matrix and $\mathbf{x}\in \mathbb{R}^n$) that I want to minimize given the normalization ...
3
votes
1answer
688 views

Power Iteration on general matrices (with higher multiplicity of dominant eigenvalue)

To compute the eigenvector corresponding to a dominant eigenvalue of a matrix $A\in\mathbb{R}^{n\times n}$, one could apply the Power Iteration: $$v_1=\frac{Av_1}{\|Av_1\|}.$$ 1) in case $A$ is ...
3
votes
1answer
193 views

Manipulating a generalized eigenvector problem to plain eigenvector problem

Let $X\in\mathbb{R}^{n\times p}$ denote a matrix with $p$ linearly-independent columns, and let $L\in\mathbb{R}^{n\times n}$ denote a symmetric matrix. Furthermore, let $D\in\mathbb{R}^{n\times n}$ ...
6
votes
3answers
601 views

Eigenvectors with the Power Iteration

To compute the eigenvector corresponding to dominant eigenvalue of a symmetric matrix $A\in\mathbb{R}^{n\times n}$, one used Power Iteration, i.e., given some random initialization, ...
9
votes
2answers
293 views

Fastest way to find eigenpairs of a small nonsymmetric matrix on a GPU in shared memory

I have a problem where I need to find all positive (as in the eigenvalue is positive) eigenpairs of a small (usually smaller than 60x60) nonsymmetric matrix. I can stop calculating when the eigenvalue ...
2
votes
1answer
136 views

Root Convergence rate of Iterative Scheme

I have an iterative sequence for optimizing an EM (Expectation Maximization) algorithm based loss function $L(X)$ with $t$ being the iteration number as: $X_t=ABX_{t-1}+CX_{t-1}+X_{t-1}$ where $A$ is ...