Questions tagged [linear-algebra]

Questions on the algorithmic/computational aspects of linear algebra, including the solution of linear systems, least squares problems, eigenproblems, and other such matters.

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74 views

Element Preconditioner

Im just working on a preconditioner for the linear equation system $Ax = b$ arising in FEM for elliptic PDE. $A$ is a s.p.d Matrix with real valued entries. I read something about the element by ...
3
votes
2answers
141 views

Factoring the sum of two matrices

Given \begin{equation} A_i=B+C_i \end{equation} where $A_i$,$B$ and $C_i$, $i=1,\dotsc,N$ are large square matrices, $B$ is symmetric, $C_i$ are zero matrices aside for a square block on the diagonal. ...
-1
votes
1answer
74 views

Relation of Condition of a Matrix and Convergency

Can anybody explain me the relation between the condition of a Matrix and the convergency of a problem. For example how is the relation between the condition of the stiffness Matrix occuring in FEM ...
0
votes
1answer
42 views

Slightly change two vectors to satisfy a constraint

$\vec{a}\cdot\vec{b} \approx c$ $\vec{\alpha} \cdot \vec{\beta} = c$ $\vec{\alpha}$ is close to $\vec{a}$ and $\vec{\beta}$ is close to $\vec{b}$ Given $\vec{a}$, $\vec{b}$ and c, how to find $\vec{\...
1
vote
1answer
336 views

Why does Newton's method with Linear Equality Constraints use KKT condition?

Goal: Optimize convex function $f(\vec{x})$ subjected to constraint $A\vec{x} = \vec{b}$ starting at a point $\vec{x}_0$ that satisfies the constraint. The problem only has equality constraint. Why ...
5
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2answers
1k views

Fast and Numerically Stable Pairwise Distance Algorithms

I'm looking for resources on fast, numerically stable pairwise euclidean distance algorithms. In particular, suppose $A \in \mathbb{R}^{M \times D}$ and $B \in \mathbb{R}^{N \times D}$ are two sets of ...
1
vote
1answer
115 views

TDMA with 3rd order upwind scheme

I'm trying to implement a model I found in a paper, but there is something I do not understand. The authors say they use TDMA to solve their equations; however, they use a 3rd order upwind biased ...
2
votes
1answer
90 views

Inverting small matrices: canned factorization versus explicit formula

I am interested in solving a large number of small linear systems of equations, $Ax=b$, with $A$ either $2\times2$ or $3\times3$. Assuming none of these systems are actually singular, is there ...
1
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1answer
138 views

Solving linear system with matrix multiplication

When solving a linear system $Ax=b$ where $A=B^TCB$ do I need to form $A$ explicitly by two matrix-matrix multiplications or is there another more simple way? $C$ is a NxN matrix and not always ...
4
votes
1answer
173 views

Do I really need to invert this matrix

I need to calculate a matrix $A$ (at least some elements of it, see below) as defined by the following equation $$ A=B(\mathbb{1}-B)^{-1} $$ where B is a square matrix of dimension $N$ and $\mathbb{...
3
votes
3answers
202 views

Derive the formula for eigenvalues

If $A$ has eigenvalue $\lambda_A$ $$B = I - c\frac{I-rA}{I-\bar{r}A}$$ How to derive the eigenvalue $\lambda_B$? $$\lambda_B=1-c\frac{1-r\lambda_A}{1-\bar{r}\lambda_A}$$ where $c, r, \bar{r}$ are ...
2
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2answers
129 views

Memory/speed tradeoff for many small matrix inverses

Problem In the case of a finite element code, I have many small (order of 30x30) matrix inverses (or LU factorizations), one per finite element. These matrix inverses never change and must be applied ...
1
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1answer
125 views

Pivoting in Block LU

What are common methods to choose pivot blocks in Block LU (for non-SPD/non-Diagonally Dominant Matrices)?
7
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2answers
862 views

Sparse smallest eigenvalue problem on a linear subspace?

I am interested in methods for solving the optimization problem $$ \begin{array}{rl} \arg\min_x & x^T A x \\ \mathrm{s.t.} & x^T x = 1 \\ & Bx = 0 \end{array} $$ where $A$ is symmetric ...
1
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0answers
152 views

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 &\...
8
votes
1answer
355 views

Computing geodesic distances with diffusion

I am trying to solve an APSP (All-Pair Shortest Path) problem on a weighted graph. This graph is actually a 1, 2 or 3 dimensional grid, and the weights on each edge represent the distance between its ...
14
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6answers
1k views

Approximate spectrum of a large matrix

I want to compute the spectrum (all the eigenvalues) of a large sparse matrix (hundreds of thousands of rows). This is hard. I am willing to settle for an approximation. Are there approximation ...
8
votes
1answer
1k views

How can I compute the Schur complement in PETSc?

How can I compute the Schur complement: $$ S = K_{bb} - K_{ba} K_{aa}^{-1} K_{ab} $$ where $$ K=\begin{pmatrix} K_{aa} & K_{ab} \\ K_{ba} & K_{bb} \end{pmatrix} $$ (in some ordering) is a ...
32
votes
5answers
12k views

Performance differences between ATLAS and MKL?

ATLAS is a free BLAS/LAPACK replacement that tunes itself to the machine when compiled. MKL is the commercial library shipped by Intel. Are these two libraries comparable when it comes to performance, ...
4
votes
2answers
301 views

Moore-Penrose pseudoinverse of singular rank degenerate matrix

I am trying to attain the Moore-Penrose pseudoinverse of a very large, very sparse, rank-degenerate, singular, and square matrix. ($75000 \times 75000$, near rank). The matrix is a graph Laplacian and ...
10
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2answers
1k views

Which iterative linear solvers converge for positive semidefinite matrices?

I want to know which of the classic linear solvers (e.g Gauss-Seidel, Jacobi, SOR) are guaranteed to converge for the problem $Ax=b$ where $A$ is positive semi definite and of course $b \in im(A)$ (...
3
votes
0answers
210 views

Computing Small Eigenvalues with Sparse Symmetric Indefinite Mass Matrix

I want the eigenvalues of the following generalized eigenvalue problem: $$ Av = \lambda M v $$ where $A\in\mathbb{R}^{n\times n}$ is sparse, symmetric, and positive semi-definite $M\in\mathbb{R}^{n\...
3
votes
1answer
179 views

How many operations are needed for LAPACK's zgesv to solve a linear system?

I have a linear system of complex numbers. I am using LAPACK' zgesv (actually I am using intel MKL LAPACKE, but I am assuming the algorithm is the same). No assumption can be made about the system. I ...
1
vote
1answer
71 views

Thomas Algorithm Kernel OpenCL

I am trying to implement the Thomas algorithm using OpenCL. ...
2
votes
2answers
458 views

Lost on Matrix Inversion

I try to implement some big matrix inversion. My system configuration is Hardware:- Memory: 62.8GiB, Processor: Intel Xeon(R)CPU E5-2670 v3 @2.30GHZ*48 To implement matrix inversion I am using ...
4
votes
1answer
77 views

Method to Efficiently Solve “Centered” Least Squares without centering “A”

Suppose I want to solve $$\text{arg min}_x \frac{1}{2}\|\tilde{A}x - b\|_2^2 + \frac{1}{2}\|x - c\|_2^2$$ where $A$ is a wide sparse matrix and $\tilde{A} = A C_n = A (I - \mathbf{1}\, \mathbf{1}^T/...
1
vote
0answers
51 views

Methods to compute specific eigenvector components for a tridiagonal matrix

I have an application that is somewhat similar to the situation of computing Gaussian quadrature nodes and weights: simply put, I need to compute the eigenvalues and the last two (normalized) ...
1
vote
1answer
860 views

Ways to solve $Ax=b$ for a sparse (banded) $A$ with updates

I want to solve the time-dependent Schrodinger Equation using the Crank-Nicolson scheme. I end up with the following matrix equation ...
1
vote
1answer
301 views

Calculate partial trace of an outer product in Python?

I have a python implementation of calculating the partial trace over select dimensions. ...
7
votes
2answers
3k views

Updatable SVD implementation in Python, C, or Fortran?

I would like to do evolving factor analysis using the SVD: Given $m \times n$ data matrix $\mathcal{A}$, and for each $i$ from 1 to $m$, I want to calculate the singular values of: $$\mathcal{A}\...
4
votes
1answer
128 views

Level scheduling of triangular sparse matrices

Assume one has a triangular sparse matrix and want to solve $Lx=b$ where $b$ and $L$ are known. This can be done easily by using forward substitution when $L$ is a lower triangular matrix. Forward ...
2
votes
1answer
162 views

Can the Power Method be used here?

Given a set of $n$ points on which a triangulation is performed, it is possible to construct coefficients $\lambda_{ij}>0$ such that each point $x_i$ is a convex combination of the points connected ...
2
votes
0answers
69 views

Simultaneous update to barycenters

Suppose a tiling is given in 2D (an embedding of a planar triangulated graph), with all faces convex. Now suppose one moves each point, one by one, to the barycenter of its neighbors. I think that ...
1
vote
1answer
210 views

How to use CSDP to express a semidefinite program?

I am trying to use CSDP and am struggling with it. Consider, for example, the following semidefinite program $$\begin{array}{ll} \text{minimize} & 0\\ \text{subject to} & Q - A' Q A - \...
4
votes
1answer
162 views

Compute bilinear form with LAPACK

I need to compute a bilinear form for a set of left and right vectors $$ w_k = \sum_{i,j} V_{ik}^*A_{ij}U_{jk},$$ where $A_{ij}\in\mathbb{R}$ and $U_{jk}, V_{ik} \in \mathbb{C}$ (Assume that all the ...
4
votes
2answers
258 views

How “sparse” should a sparse matrix be to see benefits?

I have a matrix, whose size scales as $2^N$ (assume even $N$). In each row of the matrix, only about $2^{N/2}$ of the entries are filled ($N$ can be somewhere between 10 and 40, depending on what's ...
4
votes
1answer
98 views

Compute specific eigenvalues in the complex plane with Feast?

In physical problems, it's quite common that we need to solve for specific eigenvalues in the complex plane, e.g. with a positive real part and negative imaginary part. In this case, we are looking ...
3
votes
0answers
160 views

Unstable convergence of a Poisson equation

What could be the reason that the solution of a Poisson equation is smooth when obtained by an iterative solver, only if the maximum residual is set to a high value (e.g. 0.1)? When the maximum ...
2
votes
1answer
193 views

Probability of reconstructing a word using c substrings from a random sample

Consider a voice recording split into it's phonemes as our sample $S=(s_1,...,s_k) \in \Omega = P^k$. The number of phonemes is $|P| = 40$. Then I have a word $w = (w_1,...,w_n) \in P^n$. I want to ...
5
votes
1answer
137 views

Generate approximately semi-orthogonal tall matrix approximately satisfying constraints

I have a set of matrices $\{(A_i,D_i)\}$ for $i\in\{1,\ldots,n\}$, where: Each $D_j\in\mathbb{R}^{S\times S}$ is diagonal, and every entry on the main diagonal is non-negative. Each $A_j\in\mathbb{R}^...
7
votes
1answer
228 views

Least Squares with Dense-Block Diagonal Structure

I need to solve a least squares problem that takes the following form: $$p = \arg \min_{x}\Vert J V x - y \Vert_2, $$ where $J \in \mathbb{R}^{N \times N}$ is a general dense matrix, and $V \in \...
3
votes
1answer
162 views

Diagonalizing a block tridiagonal Toepliz Hermitean matrix

I have to diagonalize, within a Fortran-written code, a block tridiagonal Toeplitz Hermitian matrix, e.g. $$ \left[ \begin{array}{ccccc} \ddots & \hat{A} & & & \\ \hat{A}^\dagger &...
4
votes
1answer
222 views

How to solve the following Frobenius norm-minimization problem?

Background We know how to solve the following minimization problem $$ \min_{X} \lVert AX - B \rVert_F^2 $$ But what about the extended version? $$ \min_{X} \lVert A \begin{bmatrix} X & X^2 \...
7
votes
2answers
206 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 ...
4
votes
1answer
162 views

Matrix exponential of hermitian matrix with eigenvectors from generalized eigenvalue problem

I want to calculate the following expression $$ \exp(-i\Delta t\mathbf{H}) $$ where $\mathbf{H}\in\mathbb{C}^{n\times n}$ is a hermitian matrix. Since I have a highly optimized eigensolver in the code ...
5
votes
1answer
2k views

Sign differences in spectral decomposition in NumPy

I am trying to understand an example from a book, but I seem to get different answers depending on which spectral decomposition function I use in NumPy. I am trying to find a spectral decomposition $...
8
votes
3answers
1k views

Optimizing matrix-vector multiplication for many small matrices

I'm looking at speeding up matrix-vector products but everything I read is about how to do it for very large matrices. My case, the matrices are small but the number of times it must be done is very ...
3
votes
1answer
196 views

How to solve the inverse problem of least-squares?

Focusing on following least squares problem: $$\min\limits_{V} \lVert Z - WV \rVert _{_F}^2$$ $$Z∈{R}^{m\times n},\quad W∈{R}^{m\times k},\quad V∈{R}^{k\times n},\quad k\lt m\lt n $$ This problem ...
8
votes
0answers
312 views

Eigenvalue with largest imaginary part

Iterative eigensolvers such as ARPACK, give the option to find a subset of the eigenvalues which have the largest imaginary part. My question is how do these algorithms work. As I understand it, ...
2
votes
1answer
495 views

Distributed (MPI) matrix matrix multiplication

I perform matrix matrix multiplications (between rank-3 and rank-2 arrays) in fortran using following subroutine, ...

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