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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
0 votes
0 answers
32 views

Finding measure representation for rank 2 moment matrices

Assuming the following equation has a solution, I'm interested in finding any concrete values of $x_{1},\dots x_{n},y_{1},\dots y_{n},c_{1},c_{2},R$ that fulfills it. $$ \begin{bmatrix} 1 & 1 \\ ...
patchouli's user avatar
  • 275
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
1 vote
1 answer
184 views

Average distance between points of lower dimensional simplices in $\mathbb R^n$

Notation: By a simplex, we mean the convex hull of a finite set of distinct points in $\mathbb R^n$, which are called the vertices of the simplex. $\mathcal H^n$ will denote the $n$-dimensional ...
Nate River's user avatar
  • 6,223
1 vote
0 answers
61 views

Discrete-to-continuum convergence of principal Fokker-Planck eigenvalues

I am looking for a reference justifying the following statement. Let $L^n$ be any "reasonably consistent" finite-difference approximation of the Fokker-Planck operator in dimension $d=1$ $$ ...
leo monsaingeon'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
2 votes
1 answer
299 views

Product of a vector by an inverse of Toeplitz matrix

It is well known that using fast Fourier transform it's possible to multiply a vector by a Toeplitz matrix $A \cdot v = w$ in $n\cdot\log(n)$ operations. I read somewhere that also the product of a ...
Enea Olati's user avatar
1 vote
0 answers
77 views

Find a vector in the null space of a large dense matrix, where elements in the matrix are not directly accessible

I am working with Conjugate Gradient method to solve for 𝐴𝑥=𝑏, where 𝐴 is an extremely large PSD and Singular matrix. I cannot directly access the elements of 𝐴. The only thing I can do is ...
HANDSOMEJACKANDY's user avatar
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
0 votes
0 answers
108 views

Solving a nonlinear equation maybe with Lambert W function

Can you please help me solve the following nonlinear equation? \begin{equation} \boldsymbol{z} \odot\left(\boldsymbol{\Gamma}^{\top} \boldsymbol{y}\right)=(\beta)^{\frac{1}{m-1}}\left(\frac{m-1}{...
Iman Nodozi's user avatar
6 votes
1 answer
913 views

Resultant of linear combinations of Chebyshev polynomials of the second kind

The Chebyshev polynomial $U_n(x)$ of the second kind is characterized by $$ U_n(\cos\theta)=\frac{\sin(n+1)\theta}{\sin(\theta)}. $$ It seems that $$\operatorname*{Res}_x \left( U_n(x)+tU_{n-1}(x),\...
W. Wang's user avatar
  • 437
3 votes
1 answer
175 views

Is there a classical textbook/reference on numerical discretization schemes?

I found that it is relatively easy to find a book that discusses Euler discretization or Runge-Kutta discretization, but I am not aware of one that is well-known and/or common knowledge (i.e., field-...
Sin Nombre's user avatar
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
0 votes
1 answer
123 views

Explicit expression of Padé–Hermite approximant of type I

It is well known that the Padé approximants $(P,Q)$ of an analytic function in the neighborhood of $0$ can be expressed as a quotient of Hankel determinants built on the coefficients of the function $...
joaopa's user avatar
  • 3,998
0 votes
0 answers
114 views

Degeneracies in linear combination of tensor product of Pauli matrices

Let $P_i \in \{I,X,Y,Z\}^{\otimes n} $, that is $P_i = \bigotimes_{i =1 }^n \sigma_i$ with $\sigma_i \in \{I,X,Y,Z\}$, where $$ I = \begin{pmatrix} 1 & 0 \\ 0 & 1 \end{pmatrix} \hspace{1cm} X =...
KAJ226's user avatar
  • 131
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
4 votes
1 answer
721 views

Singular value decomposition of truncated discrete Fourier transform matrix

Let $\mathbf{F}$ be a discrete Fourier transform (DFT) matrix such that \begin{align} F_{m,n}=e^{-j2\pi(m-1)(n-1)/N},\quad m,n=1,\ldots,N. \end{align} What we can say about the singular value ...
Math_Y's user avatar
  • 287
4 votes
1 answer
408 views

Proof of Levinson-Durbin algorithm

Is there any article or reference book with a full proof of the Levinson-Durbin algorithm used for solving linear system with a Toeplitz matrix ?
jhn142143's user avatar
2 votes
0 answers
71 views

What are desirable properties that data should satisfy to reasonably use the dynamic mode decomposition?

In the dynamic mode decomposition, we consider a sequence of data vectors $\{z_0, \dots, z_m\}$ where $z_k \in \mathbb{R}^n$ for all $n$. We assume that the data satisfies the linear relationship $z_{...
Powerspawn's user avatar
0 votes
0 answers
135 views

Is there an efficient algorithm to project a vector onto the eigenbasis of a symmetric matrix?

Let $H$ be a symmetric matrix over $\mathbb R^n$. Given some vector $u$, I would like to express $u$ in the eigenbasis for $H$. Can this be done efficiently, perhaps using some kind of iterative ...
Jack M's user avatar
  • 623
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
1 vote
2 answers
202 views

Robust estimation of $Ax=b$

Problem setting : $ \underset{x}{\text{min}} \|Ax-b\|$, where $A \in \mathcal{R}^{m \times n}, m\gg n $, full rank. L1 loss is used for robust estimation using IRLS. The corresponding equation to ...
lalit's user avatar
  • 21
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
3 votes
1 answer
836 views

Solving multilinear equations

Let $N=\{1,2,\ldots,n\}$. Suppose we are given $n$ equations, with each equation taking the form $\sum_{A\subseteq N}\left(c_A \prod_{i\in A}x_i \right) = 0$, where each $c_A$ is a real number ...
Alexi's user avatar
  • 239
6 votes
2 answers
1k views

Is the matrix positive definite given the Gauss-Seidel method converges?

I know that the Gauss-Seidel method converges given that the matrix you want to solve is symmetric positive definite. However, I'm wondering if the "converse" of the statement is true. Namely, if $A$ ...
bernard's user avatar
  • 205
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
1 vote
0 answers
126 views

Fastest way to calculate the eigenvalues of a product of two Toeplitz matrices

I have the following problem: I need to find the fastest way to calculate the eigenvalues of a matrix that is the product of two Toeplitz matrices. $B = A U$. The first is a regular Toeplitz matrix $A$...
Enea Olati's user avatar
1 vote
0 answers
448 views

Smallest eigenvalue for large kernel matrix

I am interested in the the asymptotics of the minimum eigenvalue $\lambda_n^n$ of a class of kernel matrix $P = [ K(x_i - x_j) ]_{i,j}$, with $x_i$ equally spaced in the unit cube of $\mathbb{R}^d$. ...
KDD's user avatar
  • 151
5 votes
1 answer
644 views

A conjecture about the submatrix of orthogonal matrix

Let $U$ be an $n\times n$ orthogonal matrix, i.e. $U\in\mathbb{R}^{n \times n}$. For any non-empty ordered sets $S_1,S_2\subset\{1,2,...,n\}$, define $U_{S_1S_2}$ to be an $|S_1|\times|S_2|$ submatrix ...
neverevernever's user avatar
4 votes
1 answer
413 views

Lipschitz property of matrix function only depending on singular values

Let $f$ be a function from $\mathbb{R}^{n\times n}$ to $\mathbb{R}$ such that there exists another symmetric function $g$ (invariant under permutation of coordinates) from $\mathbb{R}^{n}$ to $\mathbb{...
neverevernever'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
2 votes
1 answer
90 views

Can a Multilayer Perceptron fit any binary function?

Consider a perceptron $F(x) = \phi(x * w - b), \ x \in \mathbb{R}^n,$ (with Heaviside activation function $\phi$) and a dataset consisting of a finite subset $\Omega \subseteq \mathbb{R}^n$ with ...
GM1's user avatar
  • 23
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
1 answer
102 views

Polynomial Eigenvalue Problem with few non-zero coefficients

Let us define a diagonal matrix $\mathbf{D}(\lambda) = diag(\lambda^{m_1}, \dots, \lambda^{m_n})$ with $\lambda\in\mathbb{C}$ and positive integers $m_1, \dots, m_n$. The generalized characteristic ...
Jiro's user avatar
  • 909
4 votes
1 answer
1k views

Inverse of matrix with blocks of ones

It seems that there is a nice inverse for matrices that can be written as a diagonal matrix plus a symmetric matrix consisting of scaled blocks of ones. Consider a real matrix of the form: $$\begin{...
Ben Golub's user avatar
  • 1,068