All Questions
54 questions
0
votes
2
answers
97
views
Optimization algorithms for Kronecker approximation of high-dimensional covariance matrices
I'm working with a high-dimensional covariance matrix and exploring Kronecker product approximations to make it computationally manageable.
Here's the setup:
I have a graph $G$ represented by a $D\...
- 20.2k
4
votes
1
answer
342
views
rank of an integer valued matrix
I make some numerical experiments, involving rank of integer valued matrices of the size about $14\times 24$. As the matrix is integer valued, theoretically there should be no room for errors. However ...
- 3,319
1
vote
0
answers
95
views
Vandermonde-type factorization of moment matrix?
Consider $n,d \in \mathbb{N}_{>0}$, there are many functions $y:\mathbb{N}^{n} \to \mathbb{R}$. Now for simplicity, we denote $y(\alpha)$ to be $y_{\alpha}$. Let $|\alpha| = \sum_{i=1}^{n}\alpha_{i}...
- 275
2
votes
1
answer
241
views
How to solve this set of equations as efficiently as possible (with "efficiently" measured in FLOPS)?
The system of equations is the following:
$$
\Gamma_i^{\ -1} = \sum_{i=1}^nA_{ij}\Gamma_j,
$$
where $\Gamma = (\Gamma_i)$ is a vector of size $n$ and $A$ is a matrix of size $n\times n$, with $n \gt ...
- 3,065
0
votes
0
answers
67
views
Concentration of bilinear forms
This is a bit vague so I'll begin by indicating the motivation. I am looking for ways to [do something interesting or useful] with the self-attention in transformer models. Ultimately the self-...
- 6,962
3
votes
1
answer
273
views
Inflection point calculation for cubic Bézier curve encounters division by zero
I've been working on finding the inflection points of a cubic Bezier curve using the method described in a paper Hain, Venkat, Racherla, and Langan - Fast, Precise Flattening of Cubic Bézier Segment ...
- 7,226
2
votes
1
answer
217
views
How to do LU factorization efficiently based on the factorized result added with a low-rank matrix?
Suppose a square $n\times n$, dense matrix $A^{\text{old}}$ has been factorized into $L^{\text{old}}$ and $U^{\text{old}}$ components by performing a LU decomposition $A^{\text{old}} = L^{\text{old}}U^...
- 4,219
0
votes
2
answers
131
views
Reshaping data vector into a matrix for deconvolution using a circulant matrix
Suppose we have a circulant matrix S made from pseudorandom binary sequence of length $N$ consisting of $0$'s or/and $1$'s. $1$ means that we can inject something for chemical analysis and $0$ means ...
- 1,245
0
votes
2
answers
321
views
Solving sparse linear least squares or a positive definite 5-band matrix system fast
I want to quickly solve the following linear least-squares problem
$$\min_{x \in \mathbb{R}^n} \left\| A x - b \right\|_2^2$$
with a special sparse structure where each row in $A$ has only up to $4$ ...
3
votes
1
answer
369
views
Linear system with sum of Kronecker products
Here and here, specific ways to address the equation in $x$, for $N=2$, are given:
$$\sum_{i=1}^N (A_i\otimes B_i)x=c$$
Is anything know about the case $N>2$?
I am looking in fact for an efficient ...
- 15.4k
1
vote
1
answer
59
views
Does norm of discrepancy decrease monotonously in CGLS/CGNR
I am the author of the package for tomographic reconstruction https://github.com/kulvait/KCT_cbct I have implemented CGLS/CGNR , algorithm which applies conjugate gradients on normal equation
$$
A^\...
- 151
3
votes
0
answers
122
views
Preconditioners for $Ax=y$ that rely on hierarchical statistical modeling
Solving $Ax=y$ exactly can be done as:
fit a linear autoregressive model by treating rows of $A$ as data
apply this model to $A^T y$
Imperfect predictive model corresponds to an approximate inverse ...
- 2,252
1
vote
0
answers
198
views
Complexity of singular value decomposition using matrix multiplication oracles
Suppose I have an $n\times m$ real matrix $A$, $n\ll m$ with full row rank $(\mathrm{rank}(A) = n)$. I have an oracle that can compute $Ax$ or $A^T y$ for any $x\in \mathbb{R}^m, y\in \mathbb{R}^n$. ...
- 63.9k
0
votes
1
answer
266
views
Using QR or SVD to sum up finite number of matrices
Problem
I was wondering if there are any theoretical results that tackle the following problem:
Construct the following matrices $\mathbf{\mathcal{S}_{1}},\mathbf{\mathcal{S}_{2}},\ldots,\mathbf{\...
CommunityBot
- 1
0
votes
3
answers
7k
views
Find an $N$-dimensional vector orthogonal to a given vector
I'm writing an eigensolver and I'm trying to generate a guess for the next iteration in the solve that is orthogonal to to all known eigenvectors calculated thus far. This means that if I have only ...
- 1
0
votes
0
answers
228
views
Decomposition of symmetric block matrix
I came across this question and got really interested about it. There, the OP asks whether is possible to decompose a $2n \times 2n$ block matrix:
$$ \begin{pmatrix}
X & I \\
I & Y
\end{...
11
votes
2
answers
1k
views
Existence of sparse LU decomposition of sparse matrix
Let $A$ be a sparse matrix over some field. I would like to know about the existence of LU decompositions so that $L,U$ are both sparse.
More precisely, let $A$ be an $N$-by-$N$ matrix. Suppose each ...
- 3,741
0
votes
0
answers
57
views
Numerically finding matrix approximation by lower-dimensional "pseudo-similar" matrix
Consider an $N\times N$ (real or complex) matrix $A$, and some $n<N$. Is there a good numerical algorithm that finds the set consisting of an $n\times n$ matrix $B$, an $n\times N$ matrix $I$, and ...
- 3,001
0
votes
1
answer
230
views
Solution of complex linear system
In Brubeck, Nakatsukasa, and Trefethen - Vandermonde with Arnoldi (example 3) they solve the following linear system:
$$\operatorname{Re}\left(\begin{array}{ccc}1 & \cdots & z_{1}^{n} \\ 1 &...
- 45
1
vote
1
answer
146
views
Norm of a matrix with clustered eigenvalues
On page 271 of Trefethen and Bau's Numerical Linear Algebra, it is constructed a matrix
$$A=2I_{m\times m}+0.5\cdot\frac{\text{rand}(m)}{\sqrt{m}}$$
for $m=200$, where rand(m) is an array with $m\...
6
votes
1
answer
222
views
Computing $(AA\otimes BB + AB \otimes BA)^{-1}$
Can anyone suggest a way to numerically compute the following matrix vector product?
$$u=A^{-1}b=(AA\otimes BB + AB \otimes BA)^{-1}\operatorname{vec}(C)$$
Here $AA,BB,AB,BA$ and $C$ are $d\times d$ ...
- 3,065
4
votes
2
answers
948
views
Numerically solving for pseudo inverse of non-squared Vandermonde matrix
I have a linear system to solve, set up as:
$\bf{Ax}=\bf{b}$
with a non-squared matrix A,
$
\bf{A}=
\begin{bmatrix}
1 & A_{1} & A_{1}^2 & \cdots & A_{1}^n \\
1 & A_{2} & A_{...
- 384
1
vote
1
answer
279
views
Splines linearly independent
Let $N_1:=\chi_{[0,1]}$ be defined as this characteristic function and $N_n:=N_{n-1}*N_1$ then this leads to polynomials with support $[0,n]$. These splines are well-studied click for wikipedia My ...
- 3,065
2
votes
0
answers
618
views
block diagonal approximation of (SPD) matrix
I am interested in approximating a symmetric matrix in a block diagonal form, i.e. compute just some entries of the matrix located in blocks around the diagonal. Are there any theoretical guarantees ...
- 3,065
7
votes
2
answers
3k
views
Factorizing a block symmetric matrix
Let $X,Y\in\mathbb{R}^{n\times n}$ be symmetric matrices. You may assume that $X$ is positive semidefinite and $Y$ negative semidefinite, if needed, but not that they are invertible.
I would like to ...
1
vote
1
answer
324
views
How can I find minimum and maximum eigenvalue of non-positive define matrix [closed]
There is a power iteration method, but it only returns the greatest(in absolute value) eigenvalue of matrix. So when we have negative eigenvalues it'll give wrong results.
Is there any method, which ...
- 96.4k
0
votes
0
answers
159
views
How to solve a non-local self-consistent equation
I have been struggling lately with solving numerically an equation of the form:
$$ g(x\pm x_{0}) = F[ g(x) ] $$
where $g(x)$ is a matrix satisfying the condition $g(x\to\pm\infty)=0$. My question is ...
- 63.9k
4
votes
2
answers
3k
views
Methods of solving linear system of equations, how to select the appropriate method
A linear system of equations Ax=b can be solved using various methods, namely, inverse method, Gauss/Gauss-Jordan elimination, LU factorization, EVD (Eigenvalue Decomposition), and SVD (Singular Value ...
- 141
6
votes
1
answer
218
views
Any convergence rule for ${\mathbf X}_k={\mathbf A}{\mathbf X}_{k-1}{\mathbf B}$?
We know iteration ${\mathbf X}_k=\mathbf{A}{\mathbf X}_{k-1}$ converges if the spectral radius of $\mathbf A$ is smaller than 1 (see here). Is there any known rule for iteration ${\mathbf X}_k={\...
- 12.6k
0
votes
1
answer
540
views
Computing spectrum of convex combination of SPD matrices given individual spectral decompositions
Given the spectral decompositions of a non-commuting collection of symmetric positive definite $N\times N$ matrices $$\left\{ K_{i}\right\} _{i=1}^{M}, U_{i}D_{i}U_{i}^{T}=K_{i},\quad i=1,\dots,M,$$ ...
- 133
7
votes
1
answer
356
views
Is there any sort of higher-order SVD (quadratic and above) for dimensionality reduction?
(Posted this on math.stackexchange and cross.correlated over more than a week ago, but didn't get an answer, and this is a question in my research so this seems like it might have been the better ...
- 188k
1
vote
0
answers
125
views
smallest singular value over invertible sub-matrices
Consider the matrix $M = \begin{bmatrix} A & A B \end{bmatrix} \in R^{n \times (n+m)}$, with $A \in R^{n\times n}$, $B \in R^{n \times m}$, $m < n$, $m > 1$, $A$ symmetric positive definite.
...
- 303
15
votes
9
answers
9k
views
Exponential of large matrices
I want to make a diffusion kernel, which involves $e^{\beta A}$, where A is a large matrix (25k by 25k). It is an adjacency matrix, so it's symmetric and very sparse.
Does anyone have a ...
4
votes
2
answers
383
views
Question about preconditioning
I posted the following question on stackexchange but didn't get any replies; I'm hoping perhaps someone can help me here.
I understand that for many iterative methods, convergence rates can be shown ...
- 20.2k
12
votes
2
answers
5k
views
Why Householder reflection is better than Givens rotation in dense linear algebra?
It’s obvious that Givens rotation works better with sparse matrices. But I don’t know why Householder reflection is better for dense matrices. Does it require less computations? Or it’s numerically ...
CommunityBot
- 1
0
votes
1
answer
338
views
Is spectral properties a general term for condition number?
I am reading an article about solving large sparse linear systems, in this paper it’s said that most of the iterative methods to solve $Ax = b$ are very much influenced by the spectral properties of ...
- 20.2k
4
votes
0
answers
233
views
Determining Nullspace Basis such that only one column is deleted or added as row is added or deleted, and remaining columns of basis stay the same
I would like to compute, in MATLAB, the basis Z for the nullspace of an m by n matrix A, such that if one row of A is added (resulting in A_a), the basis for A_a is n-m-1 of the n-m columns of Z, i.e.,...
CommunityBot
- 1
0
votes
0
answers
83
views
Bits of precision matrix reconstruction
We have a real rank $r$ matrix $M\in\{0,1\}^{n\times n}$.
Suppose we have diagonalized using $LMR=D$.
I want to recover a real matrix $\widetilde{M}$ such that maximum absolute entry of $\widetilde{...
- 13.9k
2
votes
2
answers
607
views
Solving a matrix equation $X=c \cdot AXA' +I$ with a diagonal corrections
I am now struggling to solve the matrix $X \in R^{n \times n}$ in the following equation:
$X=c \cdot AXA' - diag(c \cdot AXA')+ I$,
where
(1) $A \in R^{n \times n}$ is a given matrix whose element ...
- 3,741
8
votes
0
answers
481
views
Problems where Conjugate gradient works much better than GMRES
I am interested in cases where Conjugate gradient works much better than GMRES method.
In general, CG is preferable choice in many cases of SPD because it requires less storage and theoretical bound ...
- 570
7
votes
1
answer
197
views
Compute only selected components of an eigenvector
I am wondering whether it is possible to compute portions of the eigenvectors of a given (possibly very big) matrix. More formally, consider the eigenvalue problem $\mathbf{Ax} = \lambda \mathbf{x}$, ...
- 570
0
votes
1
answer
769
views
Proving that the eigenvalues of a certain matrix product are positive
Let $A$ be an $m \times n$ matrix, and define:
\begin{align*}
U &= {\rm diag} \{ \frac{1}{\beta_j} \}, \beta_j = \sum_{k=1}^m |a_{kj}|, j = 1 \dots n \\
V &= {\rm diag} \{ \frac{1}{\alpha_i} \}...
- 20.2k
4
votes
1
answer
161
views
Sensitivity of the range of a matrix
The distance between two subspaces $\mathcal{U}$ and $\widetilde{\mathcal{U}}$ is classically defined as $d(\mathcal{U},\tilde{\mathcal{U}}):=\|P-\tilde{P}\|$, where $P$ and $\tilde{P}$ are orthogonal ...
- 20.2k
1
vote
1
answer
474
views
Decompositions of sparse symmetric matrices and methods for solving large linear equations
I am writing code for solving linear equations of the form
$$A_{n\times n}\cdot x=1_n$$
where $n$ is on the order of $10^6$ and $A$ is a symmetric matrix with approx $10^3$ nonzero entries in each ...
- 3,934
1
vote
1
answer
83
views
Augmenting orthonormal system into complete orthonormal system in a numerically stable way
Let us suppose we have a, say, 10 dimensional real space with 3 orthogonal unit vectors given. How do I complete this orthonormal system with 7 additional vectors into a complete ONS in a way that is ...
- 20.2k
2
votes
1
answer
1k
views
Updating $LU$ decomposition after adding a sparse matrix
How many elements of $LU$ decomposition of a symmetric matrix change after adding a sparse symmetric matrix? Is it more efficient to recompute $LU$ decomposition after adding a sparse matrix comparing ...
- 20.2k
5
votes
0
answers
392
views
Preconditioner for finding the smallest eigenpairs of a large, but structured, matrix
I'm trying to find the eigenvector corresponding to the second smallest eigenvalue of a large $(4,000,000 \times 4,000,000)$ matrix $L$. $L$ is a graph Laplacian, with the following structure: $L = D -...
- 500
7
votes
1
answer
318
views
Who first observed that Conjugate Gradient for Symmetric Positive Definite linear systems is a Krylov method?
Conjugate gradient was originally presented in the 50's before the modern understanding of Krylov subspaces (and the resulting iterative methods) was fully realized. As such, the method was derived ...
- 188k
8
votes
1
answer
1k
views
Norm of inverse confluent Vandermonde matrix
Let $\{x_1,\dots,x_n\}$ be pairwise distinct complex numbers and $l_1+l_2+\dots+l_n=N$. The $N\times N$ confluent Vandermonde matrix is defined as
$$V=
\begin{bmatrix}
v_{1,0}&v_{2,0}&\dots&...
CommunityBot
- 1
2
votes
0
answers
184
views
Checking for error in conjugate gradient algorithm
What is a good way to check if the any numerical error is occured in conjugate gradient algorithm. Additionally why is it not suggested to check error by checking A-orthogonality of search direction ...
- 489