Eigen decomposition of a symmetric matrix

Obtain a limited number of eigenvalues and eigenvectors of sparse symmetric real matrix X.

Usage:

vec eigval = eigs_sym(X, k)
vec eigval = eigs_sym(X, k, form)
vec eigval = eigs_sym(X, k, form, opts)
vec eigval = eigs_sym(X, k, sigma)
vec eigval = eigs_sym(X, k, sigma, opts)

eigs_sym(eigval, X, k)
eigs_sym(eigval, X, k, form)
eigs_sym(eigval, X, k, form, opts)
eigs_sym(eigval, X, k, sigma)
eigs_sym(eigval, X, k, sigma, opts)

eigs_sym(eigval, eigvec, X, k)
eigs_sym(eigval, eigvec, X, k, form)
eigs_sym(eigval, eigvec, X, k, form, opts)
eigs_sym(eigval, eigvec, X, k, sigma)
eigs_sym(eigval, eigvec, X, k, sigma, opts)

k specifies the number of eigenvalues and eigenvectors.

The argument form is optional and is one of the following:

"lm": obtain eigenvalues with largest magnitude (default operation).
"sm": obtain eigenvalues with smallest magnitude (see the caveats below).
"la": obtain eigenvalues with largest algebraic value.

The argument sigma is optional; if sigma is given, eigenvalues closest to sigma are found via shift-invert mode. Note that to use sigma, both ARMA_USE_ARPACK and ARMA_USE_SUPERLU must be enabled in armadillo/config.hpp.

The opts argument is optional; opts is an instance of the eigs_opts structure:

struct eigs_opts
{
  double       tol;     // default: 0
  unsigned int maxiter; // default: 1000
  unsigned int subdim;  // default: max(2*k+1, 20)
};

tol specifies the tolerance for convergence.
maxiter specifies the maximum number of Arnoldi iterations.
subdim specifies the dimension of the Krylov subspace, with the constraint k < subdim <= X.n_rows; the recommended value is subdim >= 2*k.

The eigenvalues and corresponding eigenvectors are stored in eigval and eigvec, respectively.

If X is not square sized, an error is thrown.

If the decomposition fails:

eigval = eigs_sym(X,k) resets eigval and throws an error.
eigs_sym(eigval,X,k) resets eigval and returns a bool set to false (an error is not thrown).
eigs_sym(eigval,eigvec,X,k) resets eigval and eigvec and returns a bool set to false (an error is not thrown).

Caveats:

The number of obtained eigenvalues/eigenvectors may be lower than requested, depending on the given data.
If the decomposition fails, try first increasing opts.subdim (Krylov subspace dimension), and, as secondary options, try increasing opts.maxiter (maximum number of iterations), and/or opts.tol (tolerance for convergence), and/or k (number of eigenvalues).
For an alternative to the "sm" form, use the shift-invert mode with sigma set to 0.0.
The implementation in Armadillo 12.6 is considerably faster than earlier versions; further speedups can be obtained by enabling OpenMP in your compiler (e.g., -fopenmp in GCC and clang).

Examples

[[cpp11::register]] list eig_sym2_(const doubles_matrix<>& x,
                                   const char* method,
                                   const int& k) {
  sp_mat X = as_SpMat(x);

  sp_mat Y = X.t() * X;

  vec eigval;
  mat eigvec;

  eigs_opts opts;
  opts.maxiter = 10000;
  bool ok = eigs_sym(eigval, eigvec, Y, k, method, opts);

  writable::list out(3);
  out[0] = writable::logicals({ok});
  out[1] = as_doubles(eigval);
  out[2] = as_doubles_matrix(eigvec);

  return out;
}

Eigen decomposition of a general matrix

Obtain a limited number of eigenvalues and eigenvectors of sparse general (non-symmetric/non-hermitian) square matrix X.

Usage:

cx_vec eigval = eigs_gen(X, k)
cx_vec eigval = eigs_gen(X, k, form)
cx_vec eigval = eigs_gen(X, k, sigma)
cx_vec eigval = eigs_gen(X, k, form, opts)
cx_vec eigval = eigs_gen(X, k, sigma, opts)

eigs_gen(eigval, X, k)
eigs_gen(eigval, X, k, form)
eigs_gen(eigval, X, k, sigma)
eigs_gen(eigval, X, k, form, opts)
eigs_gen(eigval, X, k, sigma, opts)

eigs_gen(eigval, eigvec, X, k)
eigs_gen(eigval, eigvec, X, k, form)
eigs_gen(eigval, eigvec, X, k, sigma)
eigs_gen(eigval, eigvec, X, k, form, opts)
eigs_gen(eigval, eigvec, X, k, sigma, opts)

k specifies the number of eigenvalues and eigenvectors.

The argument form is optional; form is one of the following:

"lm": obtain eigenvalues with largest magnitude (default operation).
"sm": obtain eigenvalues with smallest magnitude (see the caveats below).
"lr": obtain eigenvalues with largest real part.
"sr": obtain eigenvalues with smallest real part.
"li": obtain eigenvalues with largest imaginary part.
"si": obtain eigenvalues with smallest imaginary part.

The opts argument is optional; opts is an instance of the eigs_opts structure:

struct eigs_opts
{
  double       tol;     // default: 0
  unsigned int maxiter; // default: 1000
  unsigned int subdim;  // default: max(2*k+1, 20)
};

tol specifies the tolerance for convergence.
maxiter specifies the maximum number of Arnoldi iterations.
subdim specifies the dimension of the Krylov subspace, with the constraint k < subdim <= X.n_rows; the recommended value is subdim >= 2*k.

The eigenvalues and corresponding eigenvectors are stored in eigval and eigvec, respectively.

If X is not square sized, an error is thrown.

If the decomposition fails:

eigval = eigs_gen(X, k) resets eigval and throws an error.
eigs_gen(eigval, X, k) resets eigval and returns a bool set to false (an error is not thrown).
eigs_gen(eigval,eigvec, X, k) resets eigval and eigvec and returns a bool set to false (an error is not thrown).

Caveats:

The number of obtained eigenvalues/eigenvectors may be lower than requested, depending on the given data.
If the decomposition fails, try first increasing opts.subdim (Krylov subspace dimension), and, as secondary options, try increasing opts.maxiter (maximum number of iterations), and/or opts.tol (tolerance for convergence), and/or k (number of eigenvalues).
For an alternative to the "sm" form, use the shift-invert mode with sigma set to 0.0.

Examples

[[cpp11::register]] list eig_gen2_(const doubles_matrix<>& x,
                                   const char* method,
                                   const int& k) {
  sp_mat X = as_SpMat(x);

  cx_vec eigval;
  cx_mat eigvec;

  eigs_opts opts;
  opts.maxiter = 10000;

  bool ok = eigs_gen(eigval, eigvec, X, k, method, opts);

  writable::list out(3);
  out[0] = writable::logicals({ok});
  out[1] = as_complex_doubles(eigval);
  out[2] = as_complex_matrix(eigvec);

  return out;
}

Truncated singular value decomposition

Obtain a limited number of singular values and singular vectors (truncated SVD) of a sparse matrix.

The singular values and vectors are calculated via sparse eigen decomposition of:

\[ \begin{bmatrix} 0_{n \times n} & X \\ X^T & 0_{m \times m} \end{bmatrix} \]

where $n$ and $m$ are the number of rows and columns of X, respectively.

Usage:

vec s = svds(X, k)
vec s = svds(X, k, tol)

svds(vec s, X, k)
svds(vec s, X, k, tol)

svds(mat U, vec s, mat V, sp_mat X, k)
svds(mat U, vec s, mat V, sp_mat X, k, tol)

svds(cx_mat U, vec s, cx_mat V, sp_cx_mat X, k)
svds(cx_mat U, vec s, cx_mat V, sp_cx_mat X, k, tol)

k specifies the number of singular values and singular vectors.

The singular values are in descending order.

The argument tol is optional; it specifies the tolerance for convergence, and it is passed as tol / sqrt(2) to eigs_sym.

If the decomposition fails:

s = svds(X, k) resets s and throws an error.
svds(s, X, k) resets s and returns a bool set to false (an error is not thrown).
svds(U, s, V, X, k) resets U, s, and V and returns a bool set to false (an error is not thrown).

Caveats:

svds is intended only for finding a few singular values from a large sparse matrix; to find all singular values, use svd instead.
Depending on the given matrix, svds may find fewer singular values than specified.
The implementation in Armadillo 12.6 is considerably faster than earlier versions; further speedups can be obtained by enabling OpenMP in your compiler (e.g., -fopenmp in GCC and clang).

Examples

[[cpp11::register]] list svds1_(const doubles_matrix<>& x, const int& k) {
  sp_mat X = as_SpMat(x);

  // convert all values below 0.1 to zero
  X.transform([](double val) { return (std::abs(val) < 0.1) ? 0 : val; });

  mat U;
  vec s;
  mat V;

  bool ok = svds(U, s, V, X, k);

  writable::list out(4);
  out[0] = writable::logicals({ok});
  out[1] = as_doubles(s);
  out[2] = as_doubles_matrix(U);
  out[3] = as_doubles_matrix(V);

  return out;
}

Solve a system of linear equations

Solve a sparse system of linear equations, $A \cdot X = B$, where $A$ is a sparse matrix, $B$ is a dense matrix or vector, and $X$ is unknown.

The number of rows in $A$ and $B$ must be the same.

Usage:

// A = matrix, b = vector
vec x = spsolve(A, b)
vec x = spsolve(A, b, solver)
vec x = spsolve(A, b, solver, opts)

// A = matrix, B = matrix
mat X = spsolve(A, B)

spsolve(x, A, b)
spsolve(x, A, b, solver)
spsolve(x, A, b, solver, opts)

The solver argument is optional; solver is either "superlu" (default) or "lapack".

For "superlu", ARMA_USE_SUPERLU must be enabled in armadillo/config.hpp.
For "lapack", the sparse matrix $A$ is converted to a dense matrix before using the LAPACK solver. This considerably increases memory usage.

The opts argument is optional and applicable to the SuperLU solver, and is an instance of the superlu_opts structure:

struct superlu_opts
{
  bool             allow_ugly;   // default: false
  bool             equilibrate;  // default: false
  bool             symmetric;    // default: false
  double           pivot_thresh; // default: 1.0
  permutation_type permutation;  // default: superlu_opts::COLAMD
  refine_type      refine;       // default: superlu_opts::REF_NONE
};

allow_ugly is either true or false; it indicates whether to keep solutions of systems singular to working precision.
equilibrate is either true or false; it indicates whether to equilibrate the system (scale the rows and columns of $A$ to have unit norm).
symmetric is either true or false; it indicates whether to use SuperLU symmetric mode, which gives preference to diagonal pivots.
pivot_thresh is in the range [0.0, 1.0], used for determining whether a diagonal entry is an acceptable pivot (details in SuperLU documentation).
permutation specifies the type of column permutation; it is one of:
- superlu_opts::NATURAL: natural ordering.
- superlu_opts::MMD_ATA: minimum degree ordering on structure of $A^T \cdot A$.
- superlu_opts::MMD_AT_PLUS_A: minimum degree ordering on structure of $A^T + A$.
- superlu_opts::COLAMD: approximate minimum degree column ordering.
refine specifies the type of iterative refinement; it is one of:
- superlu_opts::REF_NONE: no refinement.
- superlu_opts::REF_SINGLE: iterative refinement in single precision.
- superlu_opts::REF_DOUBLE: iterative refinement in double precision.
- superlu_opts::REF_EXTRA: iterative refinement in extra precision.

If no solution is found:

x = spsolve(A, b) resets x and throws an error.
spsolve(x, A, b) resets x and returns a bool set to false (an error is not thrown).

The SuperLU solver is mainly useful for very large and/or highly sparse matrices.

To reuse the SuperLU factorisation of $A$ for finding solutions where $B$ is iteratively changed, see the spsolve_factoriser class.

If there is sufficient memory to store a dense version of matrix $A$, the LAPACK solver can be faster.

Examples

[[cpp11::register]] doubles spsolve1_(const doubles_matrix<>& a,
                                      const doubles& b,
                                      const char* method) {
  sp_mat A = as_SpMat(a);
  vec B = as_Col(b);

  vec X = spsolve(A, B, method);

  return as_doubles(X);
}

Factorise a sparse matrix for solving linear systems

Class for factorisation of sparse matrix $A$ for solving systems of linear equations in the form $AX = B$.

Allows the SuperLU factorisation of $A$ to be reused for finding solutions in cases where $B$ is iteratively changed.

For an instance of spsolve_factoriser named as SF, the member functions are:

SF.factorise(A. opts): factorise square-sized sparse matrix $A. Optional settings are given in theoptsargument as per thespsolve()` function. If the factorisation fails, a bool set to false is returned.
SF.solve(X, B): using the given dense matrix $B$ and the computed factorisation, store in $X$ the solution to $AX = B`. If computing the solution fails, $X$ is reset and a bool set to false is returned.
SF.rcond(): return the 1-norm estimate of the reciprocal condition number computed during the factorisation. Values close to 1 suggest that the factorised matrix is well-conditioned. Values close to 0 suggest that the factorised matrix is badly conditioned.
SF.reset(): reset the instance and release all memory related to the stored factorisation; this is automatically done when the instance goes out of scope.

Caveats:

If the factorisation of $A$ does not need to be reused, use spsolve() instead.
This class internally uses the SuperLU solver; ARMA_USE_SUPERLU must be enabled in config.hpp.

Examples

[[cpp11::register]] list spsolve_factoriser1_(const doubles_matrix<>& a,
                                              const list& b) {
  sp_mat A = as_SpMat(a);

  bool status = SF.factorise(A);

  if (status == false) {
    stop("factorisation failed");
  }
  
  double rcond_value = SF.rcond();

  vec B1 = as_Col(b[0]);
  vec B2 = as_Col(b[1]);

  vec X1, X2;

  bool ok1 = SF.solve(X1, B1);
  bool ok2 = SF.solve(X2, B2);

  if (ok1 == false) {
    stop("couldn't find X1");
  }

  if (ok2 == false) {
    stop("couldn't find X2");
  }

  writable::list out(3);
  out[0] = writable::logicals({status && ok1 && ok2});
  out[1] = as_doubles(X1);
  out[2] = as_doubles(X2);

  return out;
}

Decompositions, factorisations, inverses and equation solvers (sparse matrices)

Eigen decomposition of a symmetric matrix

Examples

Eigen decomposition of a general matrix

Examples

Truncated singular value decomposition

Examples

Solve a system of linear equations

Examples

Factorise a sparse matrix for solving linear systems

Examples