I am trying to solve a simple least square of type Ax = b. The c++ eigen library offers several functionalities regarding this and I have seen some kind of solutions here: Solving system Ax=b in linear least squares fashion with complex elements and lower-triangular square A matrix and here: Least Squares Solution of Linear Algerbraic Equation Ax = By in Eigen C++
What I want to do is that using dynamic version of the matrix A and b. The elements of matrix A are floating points in my case and has 3 columns, but the number of data items (i.e. rows) will be dynamic (inside a loop).
It will be helpful to have a short code snippet of basic declaration of A, b and filling out values.
If you need dynamic matrices/vectors, just use:
MatrixXd m1(5,7); // double
VectorXd v1(23); // double
MatrixXf m2(3,5); // floating
VectorXf v2(12); // floating
Those variables will all be saved in heap.
If you need square matrices or vectors with fixed size (but be careful, they aren't dynamic!) use the following syntax:
Matrix3d m3; // double, size 3x3
Vector3d v3; // double, size 1x3
Matrix4d m4; // double, size 4x4
Vector4d v4; // double, size 1x4
Related
The following code works as expected:
matrix.cpp
// [[Rcpp::depends(RcppEigen)]]
#include <RcppEigen.h>
// [[Rcpp::export]]
SEXP eigenMatTrans(Eigen::MatrixXd A){
Eigen::MatrixXd C = A.transpose();
return Rcpp::wrap(C);
}
// [[Rcpp::export]]
SEXP eigenMatMult(Eigen::MatrixXd A, Eigen::MatrixXd B){
Eigen::MatrixXd C = A * B;
return Rcpp::wrap(C);
}
// [[Rcpp::export]]
SEXP eigenMapMatMult(const Eigen::Map<Eigen::MatrixXd> A, Eigen::Map<Eigen::MatrixXd> B){
Eigen::MatrixXd C = A * B;
return Rcpp::wrap(C);
}
This is using the C++ eigen class for matrices, See https://eigen.tuxfamily.org/dox
In R, I can access those functions.
library(Rcpp);
Rcpp::sourceCpp('matrix.cpp');
A <- matrix(rnorm(10000), 100, 100);
B <- matrix(rnorm(10000), 100, 100);
library(microbenchmark);
microbenchmark(eigenMatTrans(A), t(A), A%*%B, eigenMatMult(A, B), eigenMapMatMult(A, B))
This shows that R performs pretty well on resorting (transpose). Multiplying has some advantages with eigen.
Using the Matrix library, I can convert a normal matrix to a sparse matrix.
Example from https://cmdlinetips.com/2019/05/introduction-to-sparse-matrices-in-r/
library(Matrix);
data<- rnorm(1e6)
zero_index <- sample(1e6)[1:9e5]
data[zero_index] <- 0
A = matrix(data, ncol=1000)
A.csr = as(A, "dgRMatrix");
B.csr = t(A.csr);
A.csc = as(A, "dgCMatrix");
B.csc = t(A.csc);
So if I wanted to multiply A.csr times B.csr using eigen, how to do that in C++? I do not want to have to convert types if I don't have to. It is a memory size thing.
The A.csr %*% B.csr is not-yet-implemented.
The A.csc %*% B.csc is working.
I would like to microbenchmark the different options, and see how matrix size will be most efficient. In the end, I will have a matrix that is about 1% sparse and have 5 million rows and cols ...
There's a reason that dgRMatrix crossproduct functions are not yet implemented, in fact, they should not be implemented because otherwise they would enable bad practice.
There are a few performance considerations when working with sparse matrices:
Accessing marginal views against the major marginal orientation is highly inefficient. For instance, a column iterator in a dgRMatrix and a row iterator in a dgCMatrix need to loop through almost all elements of the matrix to find the ones in just that column or row. See this Rcpp gallery post for additional enlightenment.
A matrix cross-product is simply a dot product between all combinations of columns. This means the penalty of using a column iterator in a dgRMatrix (vs. a column iterator in a dgCMatrix) is multiplied by the number of column combinations.
Cross-product functions in R are highly optimized, and are not (in my experience) significantly faster than Eigen, Armadillo, equivalent STL variants. They are parallelized, and the Matrix package takes wonderful advantage of these optimized algorithms. I have written C++ parallelized STL cross-product variants using Rcpp structures and I don't see any increase in performance.
If you're really going this route, check out my Rcpp gallery post on Sparse Matrix structures in Rcpp. This is to be preferred to Eigen and Armadillo Sparse Matrices if memory is a concern, as Eigen and Armadillo perform a deep copy rather than a reference to an R object already existing in memory.
At 1% density, the inefficiencies of row iterators will be greater than at say 5 or 10% density. I do most of my tests at 5% density and generally binary operations take 5-10x longer for row iterators than for column iterators.
There may be applications where row-major ordering shines (i.e. see the work by Dmitry Selivanov on CSR matrices and irlba svd), but this is absolutely not one of them, in fact, so much so you are better off doing in-place conversion to get to a CSC matrix.
tl;dr: column-wise cross-product in row-major matrices is the ultimatum of inefficiency.
I'm using the Armadillo Cpp code for matrix algebra. I have an Eigenvector matrix E that I want to sort by its eigenvalues in a vector d.
mat E;
vec d;
eig_sym(d,E,Rxx);
// Sort indices of eignen values / vectors
// based on decreasing real part of eigen values.
uvec order = sort_index(-d);
// Extract top eigen vectors.
E = E(span::all,order(1,nb_sources));
I couldn't find anything related to this kind of indexing in the documentation. Indexing using a vector is such a common requirement that I'd be surprised if it isn't present in Armadillo.
What is the proper way to do this in Armadillo?
One way to do it is
E = E.cols(order(span(0,nb_sources-1)));
I wish to convert a simple 2D array into a SparseMatrix, in order to improve performance and run time, since I am dealing with an array of a size around 50,000-70,000.
So far what I have:
SparseMatrix<double> sp;
sp.resize(numCells,numCells);
double Matrix[numCells,numCells];
Matrix = Map<SparseMatrix>(Matrix,numCells,numCells);
The compiler returns type mismatch value at argument 1 in template parameter list for 'template class Eigen::Map'.
I understand I am missing something here, but I can not figure it out.
Make a dense matrix and convert it into a sparse matrix:
double matrix[numCells * numCells]; // 1d array representation of your matrix
SparseMatrix<double> sp = Map<MatrixXd>(matrix,numCells,numCells).sparseView();
I'm a new to Eigen and I'm working with sparse LU problem.
I found that if I create a vector b(n), Eigen could compute the x(n) for the Ax=b equation.
Questions:
How to display the L & U, which is the factorization result of the original matrix A?
How to insert non-zeros in Eigen? Right now I just test with some small sparse matrix so I insert non-zeros one by one, but if I have a large-scale matrix, how can I input the matrix in my program?
I realize that this question was asked a long time ago. Apparently, referring to Eigen documentation:
an expression of the matrix L, internally stored as supernodes The only operation available with this expression is the triangular solve
So there is no way to actually convert this to an actual sparse matrix to display it. Eigen::FullPivLU performs dense decomposition and is of no use to us here. Using it on a large sparse matrix, we would quickly run out of memory while trying to convert it to dense, and the time required to compute the factorization would increase several orders of magnitude.
An alternative solution is using the CSparse library from the Suite Sparse as:
extern "C" { // we are in C++ now, since you are using Eigen
#include <csparse/cs.h>
}
const cs *p_matrix = ...; // perhaps possible to use Eigen::internal::viewAsCholmod()
css *p_symbolic_decomposition;
csn *p_factor;
p_symbolic_decomposition = cs_sqr(2, p_matrix, 0); // 1 = ordering A + AT, 2 = ATA
p_factor = cs_lu(p_matrix, m_p_symbolic_decomposition, 1.0); // tol = 1.0 for ATA ordering, or use A + AT with a small tol if the matrix has amostly symmetric nonzero pattern and large enough entries on its diagonal
// calculate ordering, symbolic decomposition and numerical decomposition
cs *L = p_factor->L, *U = p_factor->U;
// there they are (perhaps can use Eigen::internal::viewAsEigen())
cs_sfree(p_symbolic_decomposition); cs_nfree(p_factor);
// clean up (deletes the L and U matrices)
Note that although this does not use expliit vectorization as some Eigen functions do, it is still fairly fast. CSparse is also very compact, it is just a single header and about thirty .c files with no external dependencies. Easy to incorporate in any C++ project. There is no need to actually include all of Suite Sparse.
If you'll use Eigen::FullPivLU::matrixLU() to the original matrix, you'll receive LU decomposition matrix. To display L and U separately, you can use method triangularView<mode>. In Eigen wiki you can find good example of it. Inserting nonzeros into matrices depends on numbers, which you wan't to put. Eigen has convenient syntax, so you can easily insert values in loop:
for(int i=0;i<size;i++)
{
for(int j=size;j>someNumber;j--)
{
matrix(i,j)=yourClass.getNextNumber();
}
}
I need to convert a MATLAB code into C++, and I'm stuck with this instruction:
a = K\F
, where K is a sparse matrix of size n x n, and F is a column vector of size n.
I know it's easy to solve that using the Eigen library - I have tried the fullPivLu() method, and I've been able to built a working snippet, using a Matrix and a Vector.
However, my K is a SparseMatrix<double> (while F is a VectorXd). My declarations:
SparseMatrix<double> K(nec, nec);
VectorXd F(nec);
and it seems that SparseMatrix doesn't have the fullPivLu() method, nor the lu() one.
I've tried, in fact, these two different approaches, taken from the documentation:
//1.
MatrixXd x = K.fullPivLu().solve(F);
//2.
VectorXf x;
K.lu().solve(F, &x);
They don't work, because fullPivLu() and lu() are not members of 'Eigen::SparseMatrix<_Scalar>'
So, I am asking: is there a way to solve a system of linear equations (the MATLAB's mldivide, or '\'), using Eigen for C++, with K being a sparse matrix?
Thank you for any help.
Would Eigen::SparseLU work for you?