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2 answers
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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\...
JJbox's user avatar
  • 1
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 ...
Dmitri Scheglov's user avatar
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}...
patchouli's user avatar
  • 275
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-...
Felix Goldberg's user avatar
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 ...
Ziamor's user avatar
  • 133
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^...
Alex Joe's user avatar
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 ...
ACR's user avatar
  • 879
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 ...
Lilla's user avatar
  • 235
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 ...
Yaroslav Bulatov's user avatar
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^\...
VojtaK's user avatar
  • 151
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 $x\in \mathbb{R}^m, y\in \mathbb{R}^n$. ...
AspiringMat's user avatar
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{...
InMathweTrust's user avatar
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{\...
Mykael Yuday's user avatar
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 ...
Andi Bauer's user avatar
  • 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 &...
Gaussian's user avatar
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 ...
Simon's user avatar
  • 21
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\...
Leibniz's user avatar
  • 13
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 $d\times d$ ...
Yaroslav Bulatov's user avatar
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 ...
Matt Hastings's user avatar
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 ...
Foivos's user avatar
  • 335
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 ...
Dima Shkad's user avatar
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 ...
Zarathustra's user avatar
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 ...
Mohaqiq's user avatar
  • 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={\...
Tony's user avatar
  • 272
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 $$\left\{ K_{i}\right\} _{i=1}^{M}, U_{i}D_{i}U_{i}^{T}=K_{i},\quad i=1,\dots,M,$$ ...
nothing's user avatar
  • 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 ...
user650261's user avatar
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. ...
yon's user avatar
  • 303
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_{...
nimamura's user avatar
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 ...
user2813942's user avatar
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 ...
lino's user avatar
  • 253
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 ...
lino's user avatar
  • 253
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.,...
Mark L. Stone's user avatar
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 $4$ ...
sellibitze's user avatar
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 ...
Physicist 2.0's user avatar
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{...
Turbo's user avatar
  • 13.9k
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 $\mathbf{Ax} = \lambda \mathbf{x}$, ...
gboukensha's user avatar
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} \}...
t_h's user avatar
  • 3
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 ...
Federico Poloni's user avatar
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 ...
John Smith's user avatar
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 ...
Michael's user avatar
  • 2,205
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 ...
MrX's user avatar
  • 45
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 ...
Michael's user avatar
  • 2,205
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 -...
Jeff's user avatar
  • 500
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 ...
arbitUser1401's user avatar
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 ...
Kirk S.'s user avatar
  • 325
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&...
dima's user avatar
  • 959
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 ...
arbitUser1401's user avatar
0 votes
1 answer
193 views

Ease of calculation of norm

I have SPD matrix A and two vectors z and b. Is there exist a norm where I can calculate $||A^{1/2}b-z||$ without having to calculate $A^{1/2}b$ explicitly ?
arbitUser1401's user avatar
5 votes
0 answers
160 views

reference for perturbation of projection result

Let $A$ and $B$ have the same rank and dimensions. If $P_A$ denotes the projection onto the range space of $A$, then $$ \|P_A - P_B\|_2 \leq \|A - B\| \cdot \min (\|A^\dagger\|_2, \|B^\dagger\|_2). $$ ...
AatG's user avatar
  • 922
2 votes
1 answer
815 views

A question for solutions of perturbed linear systems

Consider a linear system $$Ax=b\qquad (*)$$ and a sequence of perturbed linear systems $$(A+\delta A_n)x=b+\delta b_n. \qquad (n)$$ Suppose that all the linear systems are consistent (i.e., ...
user11870's user avatar
  • 227