imate.logdet

imate.logdet(A, gram=False, p=1.0, return_info=False, method='cholesky', **options)

Log-determinant of non-singular matrix or linear operator.

Given the matrix or the linear operator \(\mathbf{A}\) and the real exponent \(p\), the following is computed:

\[\mathrm{logdet} \left(\mathbf{A}^p \right) = p \log_e \vert \det (\mathbf{A}) \vert.\]

If gram is True, then \(\mathbf{A}\) in the above is replaced by the Gramian matrix \(\mathbf{A}^{\intercal} \mathbf{A}\). In this case, if the matrix \(\mathbf{A}\) is square, then the following is instead computed:

\[\mathrm{logdet} \left((\mathbf{A}^{\intercal}\mathbf{A})^p \right) = 2p \log_e \vert \det (\mathbf{A}) \vert.\]

If \(\mathbf{A} = \mathbf{A}(t)\) is a linear operator of the class imate.AffineMatrixFunction with the parameter \(t\), then for an input tuple \(t = (t_1, \dots, t_q)\), an array output of the size \(q\) is returned, namely:

\[\mathrm{logdet} \left((\mathbf{A}(t_i))^p \right), \quad i=1, \dots, q.\]

Parameters:

Anumpy.ndarray, scipy.sparse, imate.Matrix, or imate.AffineMatrixFunction

A non-singular sparse or dense matrix or linear operator. The linear operators imate.Matrix and imate.AffineMatrixFunction can be used only if method=slq. See details in slq method. If method=cholesky, the matrix A should be positive-definite. If method=slq and gram=False, the input matrix A should be symmetric. If gram=True, the matrix can be non-square.

grambool, default=False

If True, the log-determinant of the Gramian matrix, \((\mathbf{A}^{\intercal}\mathbf{A})^p\), is computed. The Gramian matrix itself is not directly computed. If False, the log-determinant of \(\mathbf{A}^p\) is computed.

pfloat, default=1.0

The real exponent \(p\) in \(\mathbf{A}^p\).

return_infobool, default=False

If True, this function also returns a dictionary containing information about the inner computation, such as process time, algorithm settings, etc. See the documentation for each method for details.

method{‘eigenvalue’, ‘cholesky’, ‘slq’}, default=’cholesky’

The method of computing log-determinant. See documentation for each method:

• eigenvalue

• cholesky

• slq

options**kwargs

Extra arguments that are specific to each method. See the documentation for each method for details.

Returns:

logdetfloat or numpy.array

Log-determinant of matrix. If method=slq and if A is of type imate.AffineMatrixFunction with an array of parameters, then the output is an array.

infodict

(Only if return_info is True) A dictionary of information with at least the following keys. Further keys specific to each method can be found in the documentation of each method.

• matrix:

  • data_type: str, {float32, float64, float128}, type of the matrix data.

  • gram: bool, whether the matrix A or its Gramian is considered.

  • exponent: float, the exponent p in \(\mathbf{A}^p\).

  • size: (int, int), The size of matrix A.

  • sparse: bool, whether the matrix A is sparse or dense.

  • nnz: int, if A is sparse, the number of non-zero elements of A.

  • density: float, if A is sparse, the density of A, which is the nnz divided by size squared.

  • num_inquiries: int, The size of inquiries of each parameter of the linear operator A. If A is a matrix, this is always 1. For more details see slq method.

• device:

  • num_cpu_threads: int, number of CPU threads used in shared memory parallel processing.

  • num_gpu_devices: int, number of GPU devices used in the multi-GPU (GPU farm) computation.

  • num_gpu_multiprocessors: int, number of GPU multi-processors.

  • num_gpu_threads_per_multiprocessor: int, number of GPU threads on each GPU multi-processor.

• time:

  • tot_wall_time: float, total elapsed time of computation.

  • alg_wall_time: float, elapsed time of computation during only the algorithm execution.

  • cpu_proc_time: float, CPU processing time of computation.

• solver:

  • version: str, version of imate.

  • method: str, method of computation.

Raises:

LinAlgError

If method=cholesky and A is not positive-definite.

ImportError

If the package has not been compiled with GPU support, but gpu is True. Either set gpu to False to use the existing installed package. Alternatively, export the environment variable USE_CUDA=1 and recompile the source code of the package.

See also

imate.trace

imate.traceinv

imate.schatten

Notes

Method of Computation:

See documentation for each method below.

• eigenvalue: uses spectral decomposition. Suitable for small matrices (\(n < 2^{12}\)). The solution is exact.

• cholesky: uses Cholesky decomposition. Suitable for moderate-size matrices (\(n < 2^{15}\)). Can only be applied to positive-definite matrices. The solution is exact.

• slq: uses stochastic Lanczos quadrature (SLQ), which is a randomized algorithm. Can be used on very large matrices (\(n > 2^{12}\)). The solution is an approximation.

Input Matrix:

The input A can be either of:

• A matrix, such as numpy.ndarray, or scipy.sparse.

• A linear operator representing a matrix using imate.Matrix ( only if method=slq).

• A linear operator representing a one-parameter family of an affine matrix function \(t \mapsto \mathbf{A} + t\mathbf{B}\), using imate.AffineMatrixFunction (only if method=slq).

Output:

The output is a scalar. However, if A is the linear operator of the type imate.AffineMatrixFunction representing the matrix function \(\mathbf{A}(t) = \mathbf{A} + t \mathbf{B}\), then if the parameter \(t\) is given as the tuple \(t = (t_1, \dots, t_q)\), then the output of this function is an array of size \(q\) corresponding to the log-determinant of each \(\mathbf{A}(t_i)\).

Note

When A represents \(\mathbf{A}(t) = \mathbf{A} + t \mathbf{I}\), where \(\mathbf{I}\) is the identity matrix, and \(t\) is given by a tuple \(t = (t_1, \dots, t_q)\), by setting method=slq, the computational cost of an array output of size q is the same as computing for a single \(t_i\). Namely, the log-determinant of only \(\mathbf{A}(t_1)\) is computed, and the log-determinant of the rest of \(q=2, \dots, q\) are obtained from the result of \(t_1\) immediately.

Examples

Sparse matrix:

Compute the log-determinant of a sample sparse Toeplitz matrix created by imate.toeplitz() function.

>>> # Import packages
>>> from imate import toeplitz, logdet

>>> # Generate a sample matrix (a toeplitz matrix)
>>> A = toeplitz(2, 1, size=100)

>>> # Compute log-determinant with Cholesky method (default method)
>>> logdet(A)
138.6294361119891

Alternatively, compute the log-determinant of \(\mathbf{A}^{\intercal} \mathbf{A}\):

>>> # Compute log-determinant of the Gramian to the power of 3:
>>> logdet(A, p=3, gram=True)
831.7766166719346

Output information:

Print information about the inner computation:

>>> ld, info = logdet(A, return_info=True)
>>> print(ld)
138.6294361119891

>>> # Print dictionary neatly using pprint
>>> from pprint import pprint
>>> pprint(info)
{
    'matrix': {
        'data_type': b'float64',
        'density': 0.0298,
        'exponent': 1.0,
        'gram': False,
        'nnz': 298,
        'num_inquiries': 1,
        'size': (100, 100),
        'sparse': True
    },
    'solver': {
        'cholmod_used': True,
        'method': 'cholesky',
        'version': '0.13.0'
    },
    'device': {
        'num_cpu_threads': 8,
        'num_gpu_devices': 0,
        'num_gpu_multiprocessors': 0,
        'num_gpu_threads_per_multiprocessor': 0
    },
    'time': {
        'alg_wall_time': 0.000903537031263113,
        'cpu_proc_time': 0.0010093420000032438,
        'tot_wall_time': 0.000903537031263113
    }
}

Large matrix:

Compute log-determinant of a very large sparse matrix using SLQ method. This method does not compute log-determinant exactly, rather, the result is an approximation using Monte-Carlo sampling. The following example uses at least 100 samples.

>>> # Generate a matrix of size one million
>>> A = toeplitz(2, 1, size=1000000, gram=True)

>>> # Approximate log-determinant using stochastic Lanczos quadrature
>>> # with at least 100 Monte-Carlo sampling
>>> ld, info = logdet(A, method='slq', min_num_samples=100,
...                   max_num_samples=200, return_info=True)
>>> print(ld)
1386187.5751816272

>>> # Find the time it took to compute the above
>>> print(info['time'])
{
    'tot_wall_time': 15.908390650060028,
    'alg_wall_time': 15.890228271484375,
    'cpu_proc_time': 116.93080989600001
}

Compare the result of the above approximation with the exact solution of the log-determinant using the analytic relation for Toeplitz matrix. See imate.sample_matrices.toeplitz_logdet() for details.

>>> from imate.sample_matrices import toeplitz_logdet
>>> toeplitz_logdet(2, 1, size=1000000, gram=True)
1386294.3611198906

It can be seen that the error of approximation is \(0.0018 \%\). This accuracy is remarkable considering that the computation on such a large matrix took only a 16 seconds. Computing the log-determinant of such a large matrix using any of the exact methods (such as cholesky or eigenvalue) is infeasible.

Matrix operator:

The following example uses an object of imate.Matrix. Note that this can be only applied to method=slq. See further details in slq method.

>>> # Import matrix operator
>>> from imate import toeplitz, logdet, Matrix

>>> # Generate a sample matrix (a toeplitz matrix)
>>> A = toeplitz(2, 1, size=100, gram=True)

>>> # Create a matrix operator object from matrix A
>>> Aop = Matrix(A)

>>> # Compute log-determinant of Aop
>>> logdet(Aop, method='slq')
141.52929878934194

Affine matrix operator:

The following example uses an object of imate.AffineMatrixFunction to create the linear operator:

\[t \mapsto \mathbf{A} + t \mathbf{I}\]

Note that this can be only applied to method=slq. See further details in slq method.

>>> # Import affine matrix function
>>> from imate import toeplitz, logdet, AffineMatrixFunction

>>> # Generate a sample matrix (a toeplitz matrix)
>>> A = toeplitz(2, 1, size=100, gram=True)

>>> # Create a matrix operator object from matrix A
>>> Aop = AffineMatrixFunction(A)

>>> # A list of parameters t to pass to Aop
>>> t = [-1.0, 0.0, 1.0]

>>> # Compute log-determinant of Aop for all parameters t
>>> logdet(Aop, method='slq', parameters=t)
array([ 68.71411681, 135.88356906, 163.44156683])