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title: "11 - Decompositions, factorisations, inverses and equation solvers (sparse matrices)"
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---

**Adapted from the official Armadillo
[documentation](https://arma.sourceforge.net/docs.html).**

# Eigen decomposition of a symmetric matrix {#eig_sym}

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

Usage:

```cpp
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:

```cpp
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

```cpp
[[cpp4r::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 {#eig_gen}

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

Usage:

```cpp
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 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:

```cpp
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

```cpp
[[cpp4r::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_complexes(eigval);
  out[2] = as_complexes_matrix(eigvec);

  return out;
}
```

# Truncated singular value decomposition {#svds}

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:

```cpp
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

```cpp
[[cpp4r::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 {#spsolve}

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:

```cpp
// 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:

```cpp
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

```cpp
[[cpp4r::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 {#spsolve_factoriser}

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 the `opts` argument as per the `spsolve()` 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

```cpp
[[cpp4r::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;
}
```
