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Questions tagged [svd]

Singular Value Decomposition (SVD) is a decomposition (factorization) of rectangular real or complex matrix into the product of a unitary rotation matrix, a diagonal scaling matrix, and a second unitary rotation matrix.

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Finding the $i$-th largest eigenvalue of a matrix

Given a large matrix $A$ with eigenvalues $\sigma_1\ge \sigma_2 \ge \dotsc $, I want to determine only a subset of these values, say $\sigma_5,\sigma_8$ and $\sigma_{19}$. Is there an algorithm that ...
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1answer
100 views

Golub-Kahan-Lanczos Bidiagonalization Procedure implementation doesn't produce bidiagonal matrix

I'm trying to implement the aforementioned procedure using this website as a reference. At the end of the page the algorithm is described as follows: I think I've mapped the given algorithm to code ...
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finding null space to a complex matrix

I need to solve the following equation: $$ \begin{pmatrix} \frac{\omega^2}{c^2}\varepsilon_x-\mu_z^{-1}k_y^2-\mu_y^{-1}k_z^2 & \mu_z^{-1}k_xk_y & \mu_y^{-1}k_xk_z\\ \mu_z^{-1}k_xk_y &\...
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101 views

How we can use CUR decomposition in place of SVD decomposition?

I have understood how CUR and SVD works, but have not been able to understand the following. How can we use CUR in place of the SVD decomposition? Do the $C$ and $R$ matrices in the CUR follow the ...
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201 views

Nystrom approximation of SVD for asymmetric matrices

Suppose I have a symmetric matrix $K$. Subdivide $K$ into pieces as $$K=\begin{pmatrix} K_{11} & K_{12} \\ K_{21} & K_{22}\end{pmatrix},$$ where $K_{21}=K_{12}^\top$. Then, the Nystrom ...
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Inverting big symmetric and singular matrices

In this post I found a very similar probem to the one I have, but not a satisfactory answer for my purposes. I have a set of matrices $C_\ell$. They are exactly symmetric by construction. ...
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1answer
343 views

Poor SVD reconstruction of singular matrix

I am trying to calculate the singular value decomposition of this matrix using numpy.linalg.svd . However, reconstructing the matrix from the SVD gives a poor ...
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154 views

Best algorithm for inversion of matrix spanning many orders of magnitude [duplicate]

I have a very similar problem to the one described in Calculate inverse of dense matrix with entries of very different magnitude. The reason why I am opening a new question is because as far as I ...
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2answers
365 views

How much regularization to add to make SVD stable?

I've been using Intel MKL's SVD (dgesvd through SciPy) and noticed that results are are significantly different when I change precision between ...
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315 views

Pseudoinverse of a large sparse matrix in r

This question was moved from Cross-Validated: https://stats.stackexchange.com/questions/274042/pseudoinverse-of-large-sparse-matrix-in-r I am trying to calculate the pseudoinverse of a large sparse ...
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1answer
2k views

Block-matrix SVD and rank bounds

Assume, we have an $m\times n$ block matrix $M=\left[\begin{array}{c c}A&C\\B&D \end{array}\right]$, where $A$ is an $m_1 \times n_1$ matrix of rank $k_A$. $B$ is an $m_2 \times n_1$ matrix ...
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113 views

Generalized eigenvalue with null space

Define $S\in\mathbb{R}^{n\times n}$ as $$S:=H+Q^\top V^{-1} Q.$$ $H,V$ are positive semidefinite. Here, $H$, $Q$, and $V$ are large, dense matrices but they are structured: I can write code for ...
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232 views

What is the fastest way to compute the sum of the singular values of a matrix?

Is there a faster way to compute the nuclear norm (trace norm, sum of singular values) of a matrix A than computing SVD(A) directly (or diagonalizing A^*A)? I am particularly interested in the case ...
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50 views

Under what circumstances does Elemental's distributed SVD not work? [closed]

I am playing around with Elemental's distributed singular value decomposition and am running into two particular issues. Building the test at tests/lapack_like/SVD.cpp, and running with ...
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2answers
125 views

Lanczos algorithms for Hermitian system with Toeplitz kernel

Basically, I am trying to compute the SVD of a large Hermitian matrix $H$ using Lanczos iteration, while $H$ consists if a Toeplitz kernel $K$, which should be able to help speed up the matrix-vector ...
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Optimal ordering in Jacobi SVD algorithm

In Jacobi SVD algorithm as given here every pair of columns of the matrix is orthogonalized until convergence. I want to know that how does the order of selection of the pair of columns affect the ...
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1answer
93 views

Condition Number of Rectangular Matrices

The 2-norm condition number can be easily extended to rectangular matrices. I'm wondering if the inequality for the product of matrices still holds in that case, i.e., $\operatorname{cond}(AB) \leq \...
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183 views

Efficient methods to solve large dense singular least square problem (linear system)

I am trying to solve a singular linear least square problem: $$minimize: \phantom{2} ||Ax-b||^2 \\ subject \phantom{2} to: \phantom{2} x \ge 0$$ Here $ A \in R^{n \times m} $, and $ n\lt m$. here m ...
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Choosing suitable polynomial degree based on information in advection stencil

I'm working on a finite volume advection scheme for unstructured meshes which uses a multidimensional polynomial weighted least squares fit for interpolating from cell centres onto faces. In 2D, the ...
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1answer
505 views

Iterative Closest Point Algorithm

I am currently working on an iterative closest point algorithm (in C++, see here). I understand the basic premise of an ICP algorithm. You have two point clouds (a target and a reference) and you ...
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1answer
346 views

Obtaining column vectors of pseudo-inverse of a matrix

I need to compute the pseudo-inverse of a very large rectangular dense matrix without any special structure or properties. I run out of memory/computing power and have no access to a large parallel ...
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1answer
168 views

Compare reconstruction of matrices using SVD

I'm interested in how much 'signal' is retained from including k singular values in a Singular Value Decomposition, but I'm having trouble conceptualizing (or ...
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1answer
424 views

On the fly/matrix free SVD of large sparse matrix

I am trying to apply SVD to large sparse matrices. I already compared the performances of Propack and irlba to those of the matlab svd and ...
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2answers
1k views

SVD of large block-hankel matrix

I am trying to do SVD of a large block-hankel matrix for model order reduction (Low rank approximation). However, I quickly run into memory issues in forming the large Block-Hankel matrix and CPU ...
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1answer
236 views

Fast algorithms for computing only the generalized singular values (but not the vectors)

I am interested in computing only the generalized singular values, and was wondering if this was faster (and by how much?) than computing the full GSVD. In particular, I was wondering what the ...
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143 views

Using Centroid decomposition instead of SVD

This paper says centroid decomposition (CD) is an approximation to singular value decomposition (SVD). First I do not understand CD yet, since code is available I just want to try it out how it works ...
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1k views

Why is Matlab's SVD faster on skinny matrices than on fat matrices?

I noticed something odd today. I have a matrix X that is very skinny (20800 x 200), double precision real numbers, not sparse, ...
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4answers
3k views

Incremental SVD implementation in MATLAB

Is there any library/toolbox which has implementation of incremental SVD in MATLAB. I have implemented this paper, it is fast but does not work well. I tried this but in this also error propagates ...
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1answer
275 views

Using SVD to biorthogonalize left and right eigenvectors?

I have a set of left and right eigenvectors from an nonsymmetric eigenproblem, and I'd like to biorthogonalize them. I tried Gram-Schmidt, but this fails for most cases. I then read that the SVD is ...
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2answers
504 views

Are there any popular (paralleled) implementations of Lanczos methods for SVD/eigendecomposion?

I want to use it in Matlab or Java. Will these two languages be much slower for computing the algorithm compared to C, C++, in case efficiency is an important factor? I'm aware of that there's a ...
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1answer
110 views

Are there any algorithms “incrementally remove part of data (esp., old data)” from the existing SVD model of a data?

Sometimes it is meaningful to remove the influence of some old data from a SVD-based model, so as to reflect the most updated trends and provide more accurate results. I've seen there're incremental ...
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52 views

Signal balancing using SVD: notations and implementation in R

Hibbs et. al use SVD to balance the strength of different underlying signals in gene expression data using the following decomposition: $X_{m*n} = U_{m*n} \Sigma_{n*n} V^T_{n*n}$ In this case $U$ ...
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1answer
115 views

Find a single vector in the null space

I have a single sparse matrix $A$ for which I can easily compute $Ax$ and $A^*x$ and I would like to solve $\min(||Ax||) \text{ s.t. } ||x||=1$. I know the answer is the right singular vector ...
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1answer
316 views

Any relation between the singular values of each flattening matrices and the core tensor out of Tucker decomposition?

Before I know how to do tucker decomposition, I mistakenly thought the core tensor is only from combining the singular value matrices of the flattening matrices. Yes I know it is not now. For the ...
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1answer
274 views

LSA, SVD and the Frobenius norm

In Latent Semantic Analysis one uses the SVD to perform a dimensional reduction of the term-document matrix, via the Eckart-Young theorem. Now, the rank $k$ approximation obtained by E-Y is proven to ...
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1answer
411 views

SVD and HITS Algorithm Power Iterations

As we know, computing the authority (or hub) score of HITS ranking method, means to use the following matrix equation: $$ \textbf{a}^{k}=A^T A\textbf{a}^{(k-1)} $$ and apply the power iteration ...
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457 views

Truncated SVD implementation in Java

I need the Truncated SVD implementation in java. I need to pass a matrix of doubles and an integer value representing the rank where to filter out noise. In output i need a filtered matrix of doubles. ...
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1answer
177 views

singular value decomposition of a 2 x 2 complex matrix

This should be easy, but... I would like to express the singular value decomposition of a 2 x 2 complex matrix $A$ as function of its coefficients $A_{ij}$. In "closed form", no intermediate values, ...
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433 views

Fast Eigenvalue and SVD Solver for Structured Matrices

I am looking for a fast Eigenvalue and SVD solver for small dense structured matrices (Hankel and Toeplitz). I have searched for efficient implementations in libraries like MKL but I am not able to ...
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1answer
1k views

How can I reuse the SVD of matrix A to solve LS problems for both A and its transpose via Eigen C++?

If $A\in R^{m\times n}, b\in R^m, c\in R^n$, if I need to solve the least square problems via SVD of $A$ and $A^T$, i.e. I need to solve the least square solutions to following linear systems via ...
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1answer
1k views

Purely rotational least squares match

Could anyone recommend a method for the following least-squares problem: find $R \in \mathbb{R}^{3 \times 3}$ that minimizes: $\sum\limits_{i=0}^N (Rx_i - b_i)^2 \rightarrow \min$, where $R$ is a ...
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computing the truncated SVD, one singular value/vector at a time

Is there a truncated SVD algorithm that computes the singular values one at a time? My problem: I would like to compute the first $k$ singular values (and singular vectors) of a large dense matrix $M$...
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1answer
661 views

Inverse iteration to find the null singular vector of a rank-deficient matrix

I have an $n \times n$ unsymmetric matrix $A$ that results from the discretization of an ill-posed Poisson problem, and thus is rank-deficient with null space of dimension one. I want to compute just ...
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10answers
6k views

Robust algorithm for $2 \times 2$ SVD

What is a simple algorithm for computing the SVD of $2 \times 2$ matrices? Ideally, I'd like a numerically robust algorithm, but I'll like to see both simple and not-so-simple implementations. C code ...
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1answer
908 views

Why SVD is talk about less than QR and LU for sparse matrix?

For example the C++ sparse matrix libraries I used -- Eigen and SuiteSparse, they seem not to have any SVD funcitionality for sparse matrix. So just curious, is SVD more difficult than QR/LU for ...
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3answers
252 views

Pseudo-inverse of a discretized operator with a null space?

Is there a way to understand what happens when a singular operator is discretized and inverted using the pseudoinverse (say using the SVD Moore-Penrose pseudoinverse)? For example, if we discretize ...
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1answer
1k views

Generalized Singular Value Decomposition: only compute the r largest singular values

I want to compute the Generalized Singular Value Decomposition for sparse matrices with a size of up to 1000000 x 1000000 (not necessarily square). The method is going to be used in machine learning (...
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1answer
430 views

PETSc Krylov Subspace and nullspace

How is the nullspace correction implemented in the PETSc conjugate gradient solver? I searched the source code and documentation, but could not find references on the actual implementation. For ...
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4answers
3k views

Memory efficient implementations of partial Singular Value Decompositions (SVD)

For model reduction, I want to compute the left singular vectors associated to the - say 20 - largest singular values of a matrix $A \in \mathbb R^{N,k}$, where $N\approx 10^6$ and $k\approx 10^3$. ...
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1answer
391 views

SVD regularization - ray 2D tomography

Sunny day today, isn't it? Please, I need help with my problem. I have written a program to do 2D ray tomography, according to this paper. For the result, I use formula (4.15) from the paper. Now I ...