Matrix and vector types

As already mentioned in the quick introduction Doing linear algebra with arrays, nda provides an nda::matrix and an nda::vector type. Both are specializations of the general nda::basic_array with the following features:

nda::matrix is a 2-dimensional array that belongs to the 'M' algebra.
nda::vector is a 1-dimensional array that belongs to the 'V' algebra.

Their algebras determine how matrices and vectors behave in certain operations and expressions.

Otherwise, everything that is true for nda::basic_array objects is also true for nda::matrix and nda::vector objects.

Constructing matrices and vectors

To construct a matrix or a vector, we can use the same methods as for nda::array objects. We refer the user to Example 2: Constructing arrays for more information.

For example, to construct a matrix and a vector of a certain shape, we can do

// construct a 4x3 matrix and a vector of size 3
nda::matrix<double> M(4, 3);
nda::vector<double> v(3);

Here, a \( 4 \times 3 \) matrix and a vector of size \( 3 \) are created.

Let us note that there are some Factories and transformations that are specific to matrices:

// construct a 3x3 identity matrix
auto I = nda::eye<double>(3);
std::cout << "I = " << I << std::endl;
 
// construct a 2x2 diagonal matrix
auto D = nda::diag(nda::array<int, 1>{1, 2});
std::cout << "D = " << D << std::endl;

Output:

I =
[[1,0,0]
 [0,1,0]
 [0,0,1]]
D =
[[1,0]
 [0,2]]

nda::eye constructs an identity matrix of a certain size and nda::diag takes a 1-dimensional array and constructs a square diagonal matrix containing the values of the given array.

Initializing and assigning to matrices and vectors

Again, initializing and assigning to matrices and vectors works (almost) exactly in the same way as it does for arrays (see Example 3: Initializing arrays)

The main difference occurs, when we are assigning a scalar. While for arrays and vectors the assignment is done element-wise, for matrices the scalar is assigned only to the elements on the shorter diagonal and the rest is zeroed out:

// assigning a scalar to a matrix and vector
M = 3.1415;
v = 2.7182;
std::cout << "M = " << M << std::endl;
std::cout << "v = " << v << std::endl;

Output:

M =
[[3.1415,0,0]
 [0,3.1415,0]
 [0,0,3.1415]
 [0,0,0]]
v = [2.7182,2.7182,2.7182]

Views on matrices and vectors

There are some Factories and transformations for views that are specific to matrices and vectors, otherwise everything mentioned in Example 4: Views and slices still applies:

// get a vector view of the diagonal of a matrix
auto d = nda::diagonal(D);
std::cout << "d = " << d << std::endl;
std::cout << "Algebra of d: " << nda::get_algebra<decltype(d)> << std::endl;
 
// transform an array into a matrix view
auto A = nda::array<int, 2>{{1, 2}, {3, 4}};
auto A_mv = nda::make_matrix_view(A);
std::cout << "A_mv = " << A_mv << std::endl;
std::cout << "Algebra of A: " << nda::get_algebra<decltype(A)> << std::endl;
std::cout << "Algebra of A_mv: " << nda::get_algebra<decltype(A_mv)> << std::endl;

Output:

d = [1,2]
Algebra of d: V
A_mv =
[[1,2]
 [3,4]]
Algebra of A: A
Algebra of A_mv: M

HDF5, MPI and symmetry support for matrices and vectors

We refer the user to the examples

There are no mentionable differences between arrays, matrices and vectors regarding those features.

Arithmetic operations with matrices and vectors

Here the algebra of the involved types becomes important. In section Performing arithmetic operations, we have shortly introduced how arithmetic operations are implemented in nda in terms of lazy expressions and how the algebra of the operands influences the results.

Let us be more complete in this section. Suppose we have the following objects:

nda::Array (any type that satisfies this concept): O1, O2, ...
nda::array: A1, A2, ...
nda::matrix: M1, M2, ...
nda::vector: v1, v2, ...
scalar: s1, s2, ...

Then the following operations are allowed (all operations are lazy unless mentioned otherwise)

Addition / Subtraction
- O1 +/- O2: element-wise addition/subtraction, shapes of O1 and O2 have to be the same, result has the same shape
- s1 +/- A1/A1 +/- s1: element-wise addition/subtraction, result has the same shape as A1
- s1 +/- v1/v1 +/- s1: element-wise addition/subtraction, result has the same shape as v1
- s1 +/- M1/M1 +/- s1: scalar is added/subtracted to/from the elements on the shorter diagonal of M1, result has the same shape as M1
Multiplication
- A1 * A2: element-wise multiplication, shapes of A1 and A2 have to be the same, result has the same shape
- M1 * M2: non-lazy matrix-matrix multiplication, shapes have the expected requirements for matrix multiplication, calls nda::matmul
- M1 * v1: non-lazy matrix-vector multiplication, shapes have the expected requirements for matrix multiplication, calls nda::matvecmul
- s1 * O1/O1 * s1: element-wise multiplication, result has the same shape as O1
Division
- A1 / A2: element-wise division, shapes of A1 and A2 have to be the same, result has the same shape
- M1 / M2: non-lazy matrix-matrix multiplication of M1 by the inverse of M2, shapes have the expected requirements for matrix multiplication with the additional requirement that M2 is square (since M2 is inverted), result is also square with the same size
- O1 / s1: element-wise division, result has the same shape as O1
- s1 / A1: element-wise division, result has the same shape as A1 (note this is != A1 / s1)
- s1 / v1: element-wise division, result has the same shape as v1 (note this is != v1 / s1)
- s1 / M1: multiplies (lazy) the scalar with the inverse (non-lazy) of M1, only square matrices are allowed (since M1 is inverted), result is also square with the same size

Using linear algebra tools

In addition to the basic matrix-matrix and matrix-vector multiplications described above, nda provides some useful Linear algebra tools. While some of them have a custom implementation, e.g. nda::linalg::cross_product, most make use of the BLAS interface and LAPACK interface to call more optimized routines.

Let us demonstrate some of its features:

// take the cross product of two vectors
auto v1 = nda::vector<double>{1.0, 2.0, 3.0};
auto v2 = nda::vector<double>{4.0, 5.0, 6.0};
auto v3 = nda::linalg::cross_product(v1, v2);
std::cout << "v1 = " << v1 << std::endl;
std::cout << "v2 = " << v2 << std::endl;
std::cout << "v3 = v1 x v2 = " << v3 << std::endl;

Output:

v1 = [1,2,3]
v2 = [4,5,6]
v3 = v1 x v2 = [-3,6,-3]

Here, we first create two 3-dimensional vectors and then take their cross product.

To check that the cross product is perpendicular to v1 and v2, we can use the dot product:

// check the cross product using the dot product
std::cout << "v1 . v3 = " << nda::dot(v1, v3) << std::endl;
std::cout << "v2 . v3 = " << nda::dot(v2, v3) << std::endl;

Output:

v1 . v3 = 0

v2 . v3 = 0

The cross product can also be expressed as the matrix-vector product of a skew-symmetric matrix and one of the vectors:

\[ \mathbf{a} \times \mathbf{b} = \begin{bmatrix} 0 & -a_3 & a_2 \\ a_3 & 0 & -a_1 \\ -a_2 & a_1 & 0 \end{bmatrix} \begin{bmatrix} b_1 \\ b_2 \\ b_3 \end{bmatrix} \]

// cross product via matrix-vector product
auto M_v1 = nda::matrix<double>{{0, -v1[2], v1[1]}, {v1[2], 0, -v1[0]}, {-v1[1], v1[0], 0}};
std::cout << "M_v1 = " << M_v1 << std::endl;
auto v3_mv = M_v1 * v2;
std::cout << "v3_mv = " << v3_mv << std::endl;

Output:

M_v1 =
[[0,-3,2]
 [3,0,-1]
 [-2,1,0]]
v3_mv = [-3,6,-3]

Comparing this result to v3 above, we see that this is indeed correct.

Let us now turn to an eigenvalue problem. nda offers the convenience functions nda::linalg::eigenelements and nda::linalg::eigenvalues to obtain the eigenvalues and eigenvectors of a symmetric or hermitian matrix.

We start from the following symmetric matrix:

// define a symmetric matrix
auto M1 = nda::matrix<double>{{4, -14, -12}, {-14, 10, 13}, {-12, 13, 1}};
std::cout << "M1 = " << M1 << std::endl;

Output:

M1 =
[[4,-14,-12]
 [-14,10,13]
 [-12,13,1]]

Getting the eigenvalues and eigenvectors is quite easy:

// calculate the eigenvalues and eigenvectors of a symmetric matrix
auto [s, Q] = nda::linalg::eigenelements(M1);
std::cout << "Eigenvalues of M1: s = " << s << std::endl;
std::cout << "Eigenvectors of M1: Q = " << Q << std::endl;

Output:

Eigenvalues of M1: s = [-9.64366,-6.89203,31.5357]
Eigenvectors of M1: Q =
[[0.594746,-0.580953,0.555671]
 [-0.103704,-0.740875,-0.663588]
 [0.797197,0.337041,-0.500879]]

To check the correctness of our calculation, we us the fact that the matrix \( \mathbf{M}_1 \) can be factorized as

\[ \mathbf{M}_1 = \mathbf{Q} \mathbf{\Sigma} \mathbf{Q}^{-1} \; , \]

where \( \mathbf{Q} \) is the matrix with the eigenvectors in its columns and \( \mathbf{\Sigma} \) is the diagonal matrix of eigenvalues:

// check the eigendecomposition
auto M1_reconstructed = Q * nda::diag(s) * nda::transpose(Q);
std::cout << "M1_reconstructed = " << M1_reconstructed << std::endl;

Output:

M1_reconstructed =
[[4,-14,-12]
 [-14,10,13]
 [-12,13,1]]

Using the BLAS/LAPACK interface

While the functions in Linear algebra tools offer a very user-friendly experience, BLAS interface and LAPACK interface are more low-level and usually require more input from the users.

Let us show how to use the LAPACK interface to solve a system of linear equations. The system we want to solve is \( \mathbf{A}_1 \mathbf{x}_1 = \mathbf{b}_1 \) with

// define the linear system of equations and the right hand side matrix
auto A1 = nda::matrix<double, nda::F_layout>{{3, 2, -1}, {2, -2, 4}, {-1, 0.5, -1}};
auto b1 = nda::vector<double>{1, -2, 0};
std::cout << "A1 = " << A1 << std::endl;
std::cout << "b1 = " << b1 << std::endl;

Output:

A1 =
[[3,2,-1]
 [2,-2,4]
 [-1,0.5,-1]]
b1 = [1,-2,0]

First, we perform an LU factorization using nda::lapack::getrf:

// LU factorization using the LAPACK interface
auto ipiv = nda::vector<int>(3);
auto LU = A1;
auto info = nda::lapack::getrf(LU, ipiv);
if (info != 0) {
  std::cerr << "Error: nda::lapack::getrf failed with error code " << info << std::endl;
  return 1;
}

Here, ipiv is a pivot vector that is used internally by the LAPACK routine. Because nda::lapack::getrf overwrites the input matrix with the result, we first copy the original matrix A1 into a new one LU.

Then we solve the system by calling nda::lapack::getrs with the LU factorization, the right hand side vector and the pivot vector:

// solve the linear system of equations using the LU factorization
nda::matrix<double, nda::F_layout> x1(3, 1);
x1(nda::range::all, 0) = b1;
info = nda::lapack::getrs(LU, x1, ipiv);
if (info != 0) {
  std::cerr << "Error: nda::lapack::getrs failed with error code " << info << std::endl;
  return 1;
}
std::cout << "x1 = " << x1(nda::range::all, 0) << std::endl;

Output:

x1 = [1,-2,-2]

Since b1 is a vector but nda::lapack::getrs expects a matrix, we first copy it into the matrix x1. After the LAPACK call return, x1 contains the result in its first column.

Let's check that this is actually the solution to our original system of equations:

// check the solution

std::cout << "A1 * x1 = " << A1 * x1(nda::range::all, 0) << std::endl;

Output:

A1 * x1 = [1,-2,2.22045e-16]

Considering the finite precision of our calculations, this is indeed equal to the right hand side vector b1.

‍Note: BLAS and LAPACK assume Fortran-ordered arrays. We therefore recommend to work with matrices in the nda::F_layout to avoid any confusion.

Table of Contents

Matrix and vector types

Constructing matrices and vectors

Initializing and assigning to matrices and vectors

Views on matrices and vectors

HDF5, MPI and symmetry support for matrices and vectors

Arithmetic operations with matrices and vectors

Using linear algebra tools

Using the BLAS/LAPACK interface