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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12
votes
0answers
534 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 ...
11
votes
2answers
3k views

Eigenvalue decomposition of the sum: A (symmetric) + D (diagonal)

Suppose $A$ is a real symmetric matrix and its eigenvalue decomposition $V \Lambda V^T$ is given. It is easy to see what happens with the eigenvalues of the sum $A + cI$ where $c$ is a scalar constant ...
10
votes
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 $b \in im(A)$ (...
8
votes
1answer
670 views

Linear system solution with inequality constraints - methods?

First of all, I hope I am posting this in the correct place. If not, I'm sorry and could you please direct me to where I should post this? Problem: You are given a set of vectors, $\{\mathbf{a}^i\}_{...
8
votes
1answer
416 views

Least-squares for a diagonal matrix

This is a follow-up to a different question I asked with more detail. For $v\in\mathbb{R}^n$, denote $D_v\in\mathbb{R}^n$ as the diagonal matrix with elements in $v$. Given a "tall" matrix $B\in\...
7
votes
1answer
917 views

Sparse hermitian eigensystems: are there better techniques than Arpack or TRLan?

As a part of other work I need to solve relatively large (~1E5x1E5) and sparse (~100 non-zero elements in each raw in few blocks) hermitian eigensystems. Usually only few eigenvalues+vectors are ...
6
votes
1answer
161 views

Computing square root of diag(u)-uu'?

I need an efficient way to take square root of a matrix which is a sum of diagonal matrix and rank-1 matrix. More specifically it's the following matrix $$A=D-uu'=\text{diag}(u)-uu'$$ Where entries ...
5
votes
1answer
239 views

CHOLMOD condition number estimate

The CHOLMOD library provides a CHOLMOD_rcond function that estimates the reciprocal condition number (in the one norm) of a symmetric positive definite matrix from ...
4
votes
2answers
1k views

Null Space Projection for Singular Systems

Let $A$ be a general symmetric matrix operator and $P$ be the unique orthogonal projection onto $\operatorname{Range}(A) = \operatorname{Null}(A)^\perp$. Analytically, the system $$Ax = Pb$$ should ...
2
votes
1answer
940 views

Doubt regarding stopping criterion for Newton method

I am solving an unconstrained convex optimization problem, which can easily have a million variables. I am trying to get a working system with a toy problem of around 200 variables. I am noticing that ...
15
votes
2answers
467 views

Is there any way to do “double preconditioning”

Question: Suppose that you have two different (factored) preconditioners for a symmetric positive definite matrix $A$: $$A \approx B^TB$$ and $$A \approx C^TC,$$ where the inverses of the factors $B, ...
9
votes
4answers
2k views

Generating Symmetric Positive Definite Matrices using indices

I was trying to run test cases for CG and I need to generate: symmetric positive definite matrices of size > 10,000 FULL DENSE Using only matrix indices and if necessary 1 vector (Like $A(i,j) = \...
9
votes
3answers
4k views

Fastest algorithm to compute the condition number of a large matrix in Matlab/Octave

From the definition of condition number it seems that a matrix inversion is needed to compute it, I'm wondering if for a generic square matrix (or better if symmetric positive definite) is possible to ...
9
votes
2answers
1k views

Safe application of iterative methods on diagonally dominant matrices

Suppose the following linear system is given $$Lx=c,\tag1$$ where $L$ is the weighted Laplacian known to be positive $semi-$definite with a one dimensional null space spanned by $1_n=(1,\dots,1)\in\...
8
votes
5answers
4k views

Minimizing $\mathrm{trace}(S)+\mathrm{trace}(S^{-2})$ using CVX

In Matlab, I would like to minimize the function $$f(S)=\mathrm{trace}(S)+\mathrm{trace}(S^{-2})$$ where $S \in \mathcal{M}_{m,m}$ is symmetric and positive definite, which is definitely a convex ...
7
votes
1answer
3k views

What is the best way to multiply a diagonal matrix (in fortran)

What is the best way to compute: $$ Y = D X $$ where $D \in \mathbb{R}^{m\times m}$ is diagonal and $X \in \mathbb{C}^{m \times n}$ is general. I am mostly interested in these two cases: $m >> ...
7
votes
3answers
751 views

Solving shifted linear systems with LU factorization

I am interested in solving a sequence of shifted linear systems $(A+\sigma I)x = b$ for various values of $\sigma$. The matrix $A$ is sparse and not too large, so I have its LU factorization available....
7
votes
1answer
147 views

What are some ideas to preprocess / precondition the following linear system?

Let $A\in \mathbb{R}^{n\times n}$ symmetric and positive semidefinite, and $\omega\in \mathbb{R}\setminus\{0\}$. I am interested in solving the following linear system for a range of values of $\...
6
votes
0answers
126 views

What strategies / decompositions would be useful to solve the following linear system repeatedly if I only care about time to solution?

Let $A\in \mathbb{R}^{n\times n}$ be symmetric positive semidefinite, and $B\in \mathbb{R}^{n\times n}$ be symmetric positive definite. Suppose $B$ is block diagonal so it is easy to invert. (We ...
6
votes
2answers
6k views

Recommendations for a usable, fast Java matrix library?

This complements an earlier question on usable, fast C++ matrix libraries. I've looked at the Java Matrix Benchmark, and it seems like the performance of java matrix libraries is all over the place. ...
5
votes
2answers
244 views

Solving “Hadamard systems”

Suppose we have two matrices $A$ and $B$ (we can assume they're symmetric; if absolutely necessary I think they may be positive definite). Then, is there any technique for solving $$(A\circ B)x=b,$$ ...
3
votes
2answers
2k views

How to find the nearest/a near positive definite from a given matrix?

I'm given a matrix. How do I find the nearest (or a near) positive definite from it? The matrix can have complex eigenvalues, not be symmetric, etc. However, all its entries are real valued. The ...
3
votes
1answer
925 views

Spectral decomposition with eigenvalue shift

Suppose a square, real and symmetric matrix $G\in\mathbb{R}^{n\times n}$ is given, and it is known to have one zero eigenvalue associated with all ones eigenvector, $1_n$. I'm aware that the (possibly)...
3
votes
0answers
184 views

Find constrained vectors maximizing angles between them - methods?

This is related to a question I had asked earlier, with the distinction that earlier I did not have a non-linear objective functional to minimize. The problem is reproduced below with added ...
3
votes
2answers
154 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. ...
3
votes
1answer
123 views

Analytic formula for leading eigenvector of $uu^T + vv^T$?

Let $u$ and $v$ be nonzero column vectors of size $n$ and consider the $n \times n$ positive-definite matrix $A:=uu^T + vv^T$. In this post https://math.stackexchange.com/a/112201/168758, the ...
3
votes
1answer
158 views

Implementation of a $O(n \log(n))$ method to compute eigenvalues of real symmetric tridiagonal matrices

I just came upon this paper, which details the implementation of a fast method to get eigenvalues of tridiagonal symmetric matrices : Coakley, Ed S.; Rokhlin, Vladimir, A fast divide-and-conquer ...
3
votes
2answers
2k 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 ...
2
votes
2answers
448 views

Precision loss in Matrix-Vector product when applying Finite-Difference scheme

I am applying a 6th order Finite-Difference differentiation scheme as seen in http://www.scholarpedia.org/article/Method_of_lines/example_implementation/dss006 There seems to be severe numerical/...
2
votes
3answers
344 views

Generating Random Orthogonal Matrices in C++

I'm looking for an open-source library for the generation of random n-dimensional orthogonal matrices in C++. In python, it looks like such a function is available in the NumPy package. But I was not ...
1
vote
3answers
1k views

sorting adjacency matrix by the Fiedler vector

I have a fairly sparse adjacency matrix showing connections between approx 5,000 points in my dataset. I'm looking at various ways to analyze the relationships between the data points. This approach ...
0
votes
1answer
63 views

Analytic formula for $\arg\max_{\|z\|_\infty \le 1}z^T A z$, where $A=uu^T+vv^T$

Let $u$ and $v$ be column vectors of size $n \gg 1$ (not both zero), and consider the matrix $A:=uu^T+vv^T$ Question What is an analytic formula for $\arg\max_{\|z\|_\infty \le 1}z^TAz=\arg\max_{\|z\...
11
votes
4answers
2k views

Finding the square root of a Laplacian matrix

Suppose the following matrix $A$ is given $$ \left[\begin{array}{ccc} 0.500 & -0.333 & -0.167\\ -0.500 & 0.667 & -0.167\\ -0.500 & -0.333 & 0.833\end{array}\right]$$ with ...
10
votes
2answers
1k views

Is the maximum/minimum principle of the heat equation maintained by the Crank-Nicolson discretization?

I'm using the Crank-Nicolson finite difference scheme to solve a 1D heat equation. I'm wondering if the maximum/minimum principle of the heat equation (i.e. that the maximum/minimum occurs at the ...
9
votes
1answer
382 views

Solving a system with a small rank diagonal update

Suppose I have the original large, sparse linear system: $A\textbf{x}_0=\textbf{b}_0$. Now, I do not have $A^{-1}$ as A is too large to factor or any sort of decomposition of $A$, but assume that I ...
9
votes
1answer
600 views

Matrix Balancing Algorithm

I have been writing a control system toolbox from scratch and purely in Python3 (shameless plug : harold ). From my past research, I have always complaints about ...
9
votes
1answer
2k views

Schrodinger equation with periodic boundary conditions

I have a couple of questions regarding the following: I am trying to solve the Schrodinger equation in 1D using the crank nicolson discretization followed by inverting the resulting tridiagonal ...
9
votes
0answers
190 views

Imbalance of variables in Mixing Newton's method and Linear solver for a Non-linear system

Problem Solving a non-linear system of equations. The number of variables is the same as the number of equations. When I fix a set of variables (say $\vec{y}$) and keep another set free (say $\vec{...
7
votes
1answer
575 views

full rank update to cholesky decomposition for multivariate normal distribution

This question is a specialization of full rank update to cholesky decomposition, to which I hope to get a more positive answer. When calculating the minus log of the multivariate normal distribution, ...
6
votes
2answers
564 views

Applying matrix square root inverse in matrix-free regime

Let $A$ be a large symmetric positive definite matrix, and suppose that we can efficiently apply $A$ and have a fast solver to apply $A^{-1}$, but we do not have access to the matrix entries for ...
6
votes
2answers
271 views

Is it possible to ignore/discard part of a matrix when finding eigenvalues?

I have have multiple large matrices for which I need to find the largest absolute eigenvalue. I know that there is a large submatrix that does not vary. Is it possible to ignore/discard the submatrix? ...
6
votes
2answers
2k views

Sparse Incomplete Cholesky

I'm looking for an efficient, multicore, library to do incomplete cholesky (possibly modified). Many ILU code exists, but I can't find much about IC except in PETSC or Pastix. Could some of you drop ...
6
votes
1answer
1k views

generalized eigenvalue problem

I need to solve a real generalized eigenvalue problem $Ax= \lambda Bx(*)$ A and B are calculated from equations below: $$A=\sum_{i,j=1}^{N}W_{ij}(K_{i}-K_{j})\beta\beta^{T}(K_{i}-K_{j})^{T}$$ $$B=\...
6
votes
1answer
627 views

Estimate extreme eigenvalues with CG

CG may be used to estimate the extremal eigenvalues of a SPD matrix (by computing eigenvalues of tridiagonal matrix associated with the Lanczos algorithm). After a few iterations the largest ...
6
votes
1answer
378 views

Caveats of Hessian free method

Hessian free iterative optimization techniques like Newton-CG, do not explicitly compute the Hessian but instead approximate the product of the Hessian with a vector through finite difference. The ...
6
votes
3answers
2k views

Efficient eigen-decomposition of covariance matrix

I am looking for an C/C++/Python algorithm implementation that calculates eigenvalues and eigenvectors of a symmetric, positive semidefinite covariance matrix. A general-purpose eigen-decomposition ...
6
votes
1answer
790 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 ...
6
votes
1answer
206 views

Does mean removal increase accuracy of numerical differentiation?

I wish to compute the derivative of a vector through numerical differentiation. Let's say, we use a standard 2nd order central difference scheme, to arrive at a differentiation matrix, and apply it on ...
6
votes
3answers
490 views

How to solve a small least-squares problem

This question is not very deep. Suppose I have a small rectangular matrix $A$, with number of rows and columns between $50$-$100$, respectively. Given a right-hand side $b$, I want to solve the least-...
5
votes
1answer
189 views

Symmetric matrix which satisfies conditions of the form $v_i^T X v_i = 0$

I want to solve an underdetermined system of linear equations $A x = b$ with $A \in \mathbb{R}^{n \times r^2}, x \in \mathbb{R}^{r^2}, b \in \mathbb{R}^n$. The matrix $A$ has the following additional ...