Community Profile

# Andrew Knyazev

##### Last seen: 25日 前
40 2010 年以降の合計貢献数

http://en.wikipedia.org/wiki/Andrei_Knyazev_(mathematician)

Professional Interests: matrix computations, numerical PDEs, signal, image & video processing, data analytics and mining, data coding and transmission, material sciences, and model predictive control.

#### Andrew Knyazev's バッジ

Locally Optimal Block Preconditioned Conjugate Gradient
LOBPCG solves Hermitian partial generalized eigenvalue problems using preconditioning, as well as PCA

12ヶ月 前 | ダウンロード 12 件 |

Eigs in multinode cluster
EIGS has limited support for distributed memory, so you can run it only on a single node, but see the answer from Christine Tob...

12ヶ月 前 | 0

| 採用済み

How do read .npy files in matlab?
https://github.com/kwikteam/npy-matlab

12ヶ月 前 | 1

| 採用済み

Error returned in Eigs Function " Undefined operator '.*' "
Looks like a bug in chebfun - just make this comment at <https://www.mathworks.com/matlabcentral/fileexchange/47023-chebfun-curr...

1年以上 前 | 0

eigs does not return the eigenvalues closest to shift sigma
You may also want to try https://www.mathworks.com/matlabcentral/fileexchange/48-lobpcg-m passing your function handle to it. Co...

1年以上 前 | 0

find eigenvalues of a very large sparse matrix
If the matrix is real symmetric or Hermitian, you may also want to try https://www.mathworks.com/matlabcentral/fileexchange/48-l...

1年以上 前 | 0

LOBPCG Initial k eigenvectors approximation
See https://en.wikipedia.org/wiki/LOBPCG#Convergence_theory_and_practice

1年以上 前 | 0

eigs() runs faster for more eigenvalues of the same matrix
Please check <https://www.mathworks.com/matlabcentral/fileexchange/48-lobpcg-m> that has probably faster and more predictable co...

2年弱 前 | 1

How do i obtain only the first principal component?
https://www.mathworks.com/matlabcentral/fileexchange/48-lobpcg-m can be used as the method for calculating the eigenvector corre...

2年弱 前 | 0

Smallest non-zero eigenvalue for a generalized eigenvalue problem
Since both matrices A and B are singular, it is not an easy problem numerically. Even eig(full(A), full(B)) may give you wrong a...

2年弱 前 | 0

| 採用済み

Find max/min eigenvalue of a symmetric matrix
try http://www.mathworks.com/matlabcentral/fileexchange/48-lobpcg-m

Number of eigenvalues when using eigs
This is normal for eigs.

positive-definiteness of a huge sparse matrix
try http://www.mathworks.com/matlabcentral/fileexchange/48-lobpcg-m

how can i find k-eigenvalues faster than eig for hermitian dense matrix
try http://www.mathworks.com/matlabcentral/fileexchange/48-lobpcg-m

Take advantage of Hermitian matrices with eigs
You need to be more specific. Also, try http://www.mathworks.com/matlabcentral/fileexchange/48-lobpcg-m

| 採用済み

Parallel computing of eigs
check SLEPc and BLOPEX

Sparse solver for large symmetric matrices
both eigs and http://www.mathworks.com/matlabcentral/fileexchange/48-lobpcg-m can be used in a matrix-free fashion, only needing...

Difference between eigs and eig
Is this behavior expected? - Yes. Eigs uses a tricky method that may give the results you describe, especially for funny mat...

eigs function: incorrect eigenvectors
This is normal for eigs. If you are happy with eig, just stay with it.

Why can't I compute the interior eigenvalues of a sparse matrix with "eigs" without inversion in MATLAB?
check http://www.mathworks.com/matlabcentral/fileexchange/48-lobpcg-m

How can I get the (approximate) eigenvectors of a huge matrix?
try http://www.mathworks.com/matlabcentral/fileexchange/48-lobpcg-m

How can I speed up the eigen value and eigen vector computations for a non-sparse matrix?
check http://www.mathworks.com/matlabcentral/fileexchange/48-lobpcg-m

How can the Cholesky decomposition step in eigs() be avoided without passing a matrix to eigs that is a Cholesky decomposition?
check http://www.mathworks.com/matlabcentral/fileexchange/48-lobpcg-m

ortha.m
Orthonormalization Relative to matrix A

subspace.m
Angle between subspaces.

majorization check
checks if X is (weakly) majorized by Y, where X and Y must be numeric arrays.

subspacea.m
Angles between subspaces. Canonical correlations.

pcg.m with 'null' and 'flex' options
Preconditioned Conjugate Gradients handles homogeneous equations and nonsymmetric preconditioning

Best polynomial approximation in uniform norm
For a given function on an interval, the code calculates the min-max approximation by a polynomial.