I am trying to do a simple matrix inversion operation using boost. But I
am getting an error.
Basically what I am trying to find is inversted_matrix =
inverse(trans(matrix) * matrix)
But I am getting an error
Check failed in file boost_1_53_0/boost/numeric/ublas/lu.hpp at line 299:
detail::expression_type_check (prod (triangular_adaptor<const_matrix_type,
upper> (m), e), cm2)
terminate called after throwing an instance of
'boost::numeric::ublas::internal_logic'
what(): internal logic
Aborted (core dumped)
My attempt:
#include <boost/numeric/ublas/matrix.hpp>
#include <boost/numeric/ublas/vector.hpp>
#include <boost/numeric/ublas/io.hpp>
#include <boost/numeric/ublas/vector_proxy.hpp>
#include <boost/numeric/ublas/matrix.hpp>
#include <boost/numeric/ublas/triangular.hpp>
#include <boost/numeric/ublas/lu.hpp>
namespace ublas = boost::numeric::ublas;
template<class T>
bool InvertMatrix (const ublas::matrix<T>& input, ublas::matrix<T>& inverse) {
using namespace boost::numeric::ublas;
typedef permutation_matrix<std::size_t> pmatrix;
// create a working copy of the input
matrix<T> A(input);
// create a permutation matrix for the LU-factorization
pmatrix pm(A.size1());
// perform LU-factorization
int res = lu_factorize(A,pm);
if( res != 0 )
return false;
// create identity matrix of "inverse"
inverse.assign(ublas::identity_matrix<T>(A.size1()));
// backsubstitute to get the inverse
lu_substitute(A, pm, inverse);
return true;
}
int main(){
using namespace boost::numeric::ublas;
matrix<double> m(4,5);
vector<double> v(4);
vector<double> thetas;
m(0,0) = 1; m(0,1) = 2104; m(0,2) = 5; m(0,3) = 1;m(0,4) = 45;
m(1,0) = 1; m(1,1) = 1416; m(1,2) = 3; m(1,3) = 2;m(1,4) = 40;
m(2,0) = 1; m(2,1) = 1534; m(2,2) = 3; m(2,3) = 2;m(2,4) = 30;
m(3,0) = 1; m(3,1) = 852; m(3,2) = 2; m(3,3) = 1;m(3,4) = 36;
std::cout<<m<<std::endl;
matrix<double> product = prod(trans(m), m);
std::cout<<product<<std::endl;
matrix<double> inversion(5,5);
bool inverted;
inverted = InvertMatrix(product, inversion);
std::cout << inversion << std::endl;
}
Boost Ublas has runtime checks to ensure among other thing numerical stability.
If you look at source of the error, you can see that it tries to make sure that
U*X = B, X = U^-1*B, U*X = B (or smth like that) are coorect to within some epsilon. If you have too much deviation numerically this will likely not hold.
You can disable checks via -DBOOST_UBLAS_NDEBUG or twiddle with BOOST_UBLAS_TYPE_CHECK_EPSILON, BOOST_UBLAS_TYPE_CHECK_MIN.
As m has only 4 rows, prod(trans(m), m) cannot have a rank higher than 4, and as the product is a 5x5 matrix, it must be singular (i.e. it has determinant 0) and calculating the inverse of a singular matrix is like division by 0. Add independent rows to m to solve this singularity problem.
I think your matrix dimension, 4 by 5, caused the error. Like what Maarten Hilferink mentioned, you may try with a square matrix like 5 by 5. Here are requirement to have an inverse:
The matrix must be square (same number of rows and columns).
The determinant of the matrix must not be zero (determinants are covered in section 6.4). This is instead of the real number not being zero to have an inverse, the determinant must not be zero to have an inverse.
Related
I'm trying to reproduce some numpy code on Gaussian Processes (from here) using Eigen. Basically, I need to sample from a multivariate normal distribution:
samples = np.random.multivariate_normal(mu.ravel(), cov, 1)
The mean vector is currently zero, while the covariance matrix is a square matrix generated via the isotropic squared exponential kernel:
sqdist = np.sum(X1**2, 1).reshape(-1, 1) + np.sum(X2**2, 1) - 2 * np.dot(X1, X2.T)
return sigma_f**2 * np.exp(-0.5 / l**2 * sqdist)
I can generate the covariance matrix just fine for now (it can probably be cleaned but for now it's a POC):
Matrix2D kernel(const Matrix2D & x1, const Matrix2D & x2, double l = 1.0, double sigma = 1.0) {
auto dists = ((- 2.0 * (x1 * x2.transpose())).colwise()
+ x1.rowwise().squaredNorm()).rowwise() +
+ x2.rowwise().squaredNorm().transpose();
return std::pow(sigma, 2) * ((-0.5 / std::pow(l, 2)) * dists).array().exp();
}
However, my problems start when I need to sample the multivariate normal.
I've tried using the solution proposed in this accepted answer; however, the decomposition only works with covariance matrices of size up to 30x30; more than that and LLT fails to decompose the matrix. The alternative version provided in the answer also does not work, and creates NaNs. I tried LDLT as well but it also breaks (D contains negative values, so sqrt gives NaN).
Then, I got curious, and I looked into how numpy does this. Turns out the numpy implementation uses SVD decomposition (with LAPACK), rather than Cholesky. So I tried copying their implementation:
// SVD on the covariance matrix generated via kernel function
Eigen::BDCSVD<Matrix2D> solver(covs, Eigen::ComputeFullV);
normTransform = (-solver.matrixV().transpose()).array().colwise() * solver.singularValues().array().sqrt();
// Generate gaussian samples, "randN" is from the multivariate StackOverflow answer
Matrix2D gaussianSamples = Eigen::MatrixXd::NullaryExpr(1, means.size(), randN);
Eigen::MatrixXd samples = (gaussianSamples * normTransform).rowwise() + means.transpose();
The various minuses are me trying to exactly reproduce numpy's results.
In any case, this works perfectly fine, even with large dimensions. I was wondering why Eigen is not able to do LLT, but SVD works. The covariance matrix I use is the same. Is there something I can do to simply use LLT?
EDIT: Here is my full example:
#include <iostream>
#include <random>
#include <Eigen/Cholesky>
#include <Eigen/SVD>
#include <Eigen/Eigenvalues>
using Matrix2D = Eigen::Matrix<double, Eigen::Dynamic, Eigen::Dynamic, Eigen::RowMajor | Eigen::AutoAlign>;
using Vector = Eigen::Matrix<double, Eigen::Dynamic, 1>;
/*
We need a functor that can pretend it's const,
but to be a good random number generator
it needs mutable state.
*/
namespace Eigen {
namespace internal {
template<typename Scalar>
struct scalar_normal_dist_op
{
static std::mt19937 rng; // The uniform pseudo-random algorithm
mutable std::normal_distribution<Scalar> norm; // The gaussian combinator
EIGEN_EMPTY_STRUCT_CTOR(scalar_normal_dist_op)
template<typename Index>
inline const Scalar operator() (Index, Index = 0) const { return norm(rng); }
};
template<typename Scalar> std::mt19937 scalar_normal_dist_op<Scalar>::rng;
template<typename Scalar>
struct functor_traits<scalar_normal_dist_op<Scalar> >
{ enum { Cost = 50 * NumTraits<Scalar>::MulCost, PacketAccess = false, IsRepeatable = false }; };
} // end namespace internal
} // end namespace Eigen
Matrix2D kernel(const Matrix2D & x1, const Matrix2D & x2, double l = 1.0, double sigma = 1.0) {
auto dists = ((- 2.0 * (x1 * x2.transpose())).colwise() + x1.rowwise().squaredNorm()).rowwise() + x2.rowwise().squaredNorm().transpose();
return std::pow(sigma, 2) * ((-0.5 / std::pow(l, 2)) * dists).array().exp();
}
int main() {
unsigned size = 50;
unsigned seed = 1;
Matrix2D X = Vector::LinSpaced(size, -5.0, 4.8);
Eigen::internal::scalar_normal_dist_op<double> randN; // Gaussian functor
Eigen::internal::scalar_normal_dist_op<double>::rng.seed(seed); // Seed the rng
Vector means = Vector::Zero(X.rows());
auto covs = kernel(X, X);
Eigen::LLT<Matrix2D> cholSolver(covs);
// We can only use the cholesky decomposition if
// the covariance matrix is symmetric, pos-definite.
// But a covariance matrix might be pos-semi-definite.
// In that case, we'll go to an EigenSolver
Eigen::MatrixXd normTransform;
if (cholSolver.info()==Eigen::Success) {
std::cout << "Used LLT\n";
// Use cholesky solver
normTransform = cholSolver.matrixL();
} else {
std::cout << "Broken\n";
Eigen::BDCSVD<Matrix2D> solver(covs, Eigen::ComputeFullV);
normTransform = (-solver.matrixV().transpose()).array().colwise() * solver.singularValues().array().sqrt();
}
Matrix2D gaussianSamples = Eigen::MatrixXd::NullaryExpr(1, means.size(), randN);
Eigen::MatrixXd samples = (gaussianSamples * normTransform).rowwise() + means.transpose();
return 0;
}
Given a sparse matrix A and a vector b, I would like to obtain a solution x to the equation A * x = b as well as the kernel of A.
One possibility is to convert A to a dense representation.
#include <iostream>
#include <Eigen/Dense>
#include <Eigen/SparseQR>
int main()
{
// This is a toy problem. My actual matrix
// is of course bigger and sparser.
Eigen::SparseMatrix<double> A(2,2);
A.insert(0,0) = 1;
A.insert(0,1) = 2;
A.insert(1,0) = 4;
A.insert(1,1) = 8;
A.makeCompressed();
Eigen::Vector2d b;
b << 3, 12;
Eigen::SparseQR<Eigen::SparseMatrix<double>,
Eigen::COLAMDOrdering<int> > solver;
solver.compute(A);
std::cout << "Solution:\n" << solver.solve(b) << std::endl;
Eigen::Matrix2d A_dense(A);
std::cout << "Kernel:\n" << A_dense.fullPivLu().kernel() << std::endl;
return 0;
}
Is it possible to do the same directly in the sparse representation? I could not find a function kernel() anywhere except in FullPivLu.
I think #chtz's answer is almost correct, except we need to take the last A.cols() - qr.rank() columns. Here is a mathematical derivation.
Say we do a QR decomposition of your matrix Aᵀ as
Aᵀ * P = [Q₁ Q₂] * [R; 0] = Q₁ * R
where P is the permutation matrix, thus
Aᵀ = Q₁ * R * P⁻¹.
We can see that Range(Aᵀ) = Range(Q₁ * R * P⁻¹) = Range(Q₁) (because both P and R are invertible).
Since Aᵀ and Q₁ have the same range space, this implies that A and Q₁ᵀ will also have the same null space, namely Null(A) = Null(Q₁ᵀ). (Here we use the property that Range(M) and Null(Mᵀ) are complements to each other for any matrix M, hence Null(A) = complement(Range(Aᵀ)) = complement(Range(Q₁)) = Null(Q₁ᵀ)).
On the other hand, since the matrix [Q₁ Q₂] is orthonormal, Null(Q₁ᵀ) = Range(Q₂), thus Null(A) = Range(Q₂), i.e., kernal(A) = Q₂.
Since Q₂ is the right A.cols() - qr.rank() columns, you could call rightCols(A.cols() - qr.rank()) to retrieve the kernal of A.
For more information on kernal space, you could refer to https://en.wikipedia.org/wiki/Kernel_(linear_algebra)
I'm trying to use the Spectra 3.5 Library on my Linux machine, and the SparseGenMatProd wrapper for the Matrix-Vector multiplication only seems to work when the sparse matrix is in ColMajor format. Is this normal behavior and if so, how can I fix it to take RowMajor format? I've included a basic example where the output is "Segmentation fault (core dumped)". I've gone through several other posts and the documentation, but can't seem to find an answer.
#include <Eigen/Core>
#include <Eigen/SparseCore>
#include <GenEigsSolver.h>
#include <MatOp/SparseGenMatProd.h>
#include <iostream>
using namespace Spectra;
int main()
{
// A band matrix with 1 on the main diagonal, 2 on the below-main subdiagonal,
// and 3 on the above-main subdiagonal
const int n = 10;
Eigen::SparseMatrix<double, Eigen::RowMajor> M(n, n);
M.reserve(Eigen::VectorXi::Constant(n, 3));
for(int i = 0; i < n; i++)
{
M.insert(i, i) = 1.0;
if(i > 0)
M.insert(i - 1, i) = 3.0;
if(i < n - 1)
M.insert(i + 1, i) = 2.0;
}
// Construct matrix operation object using the wrapper class SparseGenMatProd
SparseGenMatProd<double> op(M);
// Construct eigen solver object, requesting the largest three eigenvalues
GenEigsSolver< double, LARGEST_MAGN, SparseGenMatProd<double> > eigs(&op, 3, 6);
// Initialize and compute
eigs.init();
int nconv = eigs.compute();
// Retrieve results
Eigen::VectorXcd evalues;
if(eigs.info() == SUCCESSFUL)
evalues = eigs.eigenvalues();
std::cout << *emphasized text*"Eigenvalues found:\n" << evalues << std::endl;
return 0;
}
If you change line 15 to:
Eigen::SparseMatrix<double, Eigen::ColMajor> M(n, n);
it will work as expected.
Currently I'm working around this and converting my matrices to ColMajor, but I'd like to understand what's going on. Any help is much appreciated.
The API of SparseGenMatProd seems to be quite misleading. Looks like you have to specify you are dealing with row-major matrices through the second template parameter:
SparseGenMatProd<double,RowMajor> op(M);
otherwise M is implicitly converted to a temporary column-major matrix which is then stored by const reference by op but this temporary is dead right after that line of code.
Right now I want to use C++ Boost to solve a matrix function: A*P=X, P=A\X. I have matrix A and matrix X, so I need to do P=A\X to get matrix P. That's a matrix division problem, right?
My C++ code is
#include "stdafx.h"
#include <boost\mat2cpp-20130725/mat2cpp.hpp>
#include <boost/numeric/ublas/matrix.hpp>
#include <boost/numeric/ublas/matrix_proxy.hpp>
#include <boost/numeric/ublas/io.hpp>
using namespace boost::numeric::ublas;
using namespace std;
int main() {
using namespace mat2cpp;
matrix<double> x(2,2); // initialize a matrix
x(0, 0) = 1; // assign value
x(1, 1) = 1;
matrix<double> y(2, 1);
y(0, 0) = 1;
y(1, 0) = 1;
size_t rank;
matrix<double> z = matrix_div(x, y, rank);
}
But it has errors Error figure, please help me! Thanks!
First of all there is no such thing as matrix division. If you have this equation A * P=X and you want to find P, than the solution is: inv(A) * A * P=inv(A) * X, where inv(A) is the inverse of the A matrix. Because we know that the inv(A) * A is equal to the identity matrix, than we can conclude that P=inv(A) * X.
Now your problem is to calculate the inverse of the A matrix. There are several ways to do this, my suggestion would be to use LU factorization.
Honestly, I don't know if the boost library has such thing as mat2cpp. If you want to use boost, I would recommend using boost/numeric/ublas/matrix.hpp.
I have a ~3000x3000 covariance-alike matrix on which I compute the eigenvalue-eigenvector decomposition (it's a OpenCV matrix, and I use cv::eigen() to get the job done).
However, I actually only need the, say, first 30 eigenvalues/vectors, I don't care about the rest. Theoretically, this should allow to speed up the computation significantly, right? I mean, that means it has 2970 eigenvectors less that need to be computed.
Which C++ library will allow me to do that? Please note that OpenCV's eigen() method does have the parameters for that, but the documentation says they are ignored, and I tested it myself, they are indeed ignored :D
UPDATE:
I managed to do it with ARPACK. I managed to compile it for windows, and even to use it. The results look promising, an illustration can be seen in this toy example:
#include "ardsmat.h"
#include "ardssym.h"
int n = 3; // Dimension of the problem.
double* EigVal = NULL; // Eigenvalues.
double* EigVec = NULL; // Eigenvectors stored sequentially.
int lowerHalfElementCount = (n*n+n) / 2;
//whole matrix:
/*
2 3 8
3 9 -7
8 -7 19
*/
double* lower = new double[lowerHalfElementCount]; //lower half of the matrix
//to be filled with COLUMN major (i.e. one column after the other, always starting from the diagonal element)
lower[0] = 2; lower[1] = 3; lower[2] = 8; lower[3] = 9; lower[4] = -7; lower[5] = 19;
//params: dimensions (i.e. width/height), array with values of the lower or upper half (sequentially, row major), 'L' or 'U' for upper or lower
ARdsSymMatrix<double> mat(n, lower, 'L');
// Defining the eigenvalue problem.
int noOfEigVecValues = 2;
//int maxIterations = 50000000;
//ARluSymStdEig<double> dprob(noOfEigVecValues, mat, "LM", 0, 0.5, maxIterations);
ARluSymStdEig<double> dprob(noOfEigVecValues, mat);
// Finding eigenvalues and eigenvectors.
int converged = dprob.EigenValVectors(EigVec, EigVal);
for (int eigValIdx = 0; eigValIdx < noOfEigVecValues; eigValIdx++) {
std::cout << "Eigenvalue: " << EigVal[eigValIdx] << "\nEigenvector: ";
for (int i = 0; i < n; i++) {
int idx = n*eigValIdx+i;
std::cout << EigVec[idx] << " ";
}
std::cout << std::endl;
}
The results are:
9.4298, 24.24059
for the eigenvalues, and
-0.523207, -0.83446237, -0.17299346
0.273269, -0.356554, 0.893416
for the 2 eigenvectors respectively (one eigenvector per row)
The code fails to find 3 eigenvectors (it can only find 1-2 in this case, an assert() makes sure of that, but well, that's not a problem).
In this article, Simon Funk shows a simple, effective way to estimate a singular value decomposition (SVD) of a very large matrix. In his case, the matrix is sparse, with dimensions: 17,000 x 500,000.
Now, looking here, describes how eigenvalue decomposition closely related to SVD. Thus, you might benefit from considering a modified version of Simon Funk's approach, especially if your matrix is sparse. Furthermore, your matrix is not only square but also symmetric (if that is what you mean by covariance-like), which likely leads to additional simplification.
... Just an idea :)
It seems that Spectra will do the job with good performances.
Here is an example from their documentation to compute the 3 first eigen values of a dense symmetric matrix M (likewise your covariance matrix):
#include <Eigen/Core>
#include <Spectra/SymEigsSolver.h>
// <Spectra/MatOp/DenseSymMatProd.h> is implicitly included
#include <iostream>
using namespace Spectra;
int main()
{
// We are going to calculate the eigenvalues of M
Eigen::MatrixXd A = Eigen::MatrixXd::Random(10, 10);
Eigen::MatrixXd M = A + A.transpose();
// Construct matrix operation object using the wrapper class DenseSymMatProd
DenseSymMatProd<double> op(M);
// Construct eigen solver object, requesting the largest three eigenvalues
SymEigsSolver< double, LARGEST_ALGE, DenseSymMatProd<double> > eigs(&op, 3, 6);
// Initialize and compute
eigs.init();
int nconv = eigs.compute();
// Retrieve results
Eigen::VectorXd evalues;
if(eigs.info() == SUCCESSFUL)
evalues = eigs.eigenvalues();
std::cout << "Eigenvalues found:\n" << evalues << std::endl;
return 0;
}