imate.InterpolateSchatten(kind=’rbf’)#

class imate.InterpolateSchatten(A, B=None, p=0, options={}, verbose=False, ti=[], kind='rbf', func_type=1)

Interpolate Schatten norm (or anti-norm) of an affine matrix function using radial basis functions (rbf) method.

See also

This page describes only the rbf method. For other kinds, see imate.InterpolateSchatten().

Parameters:
Anumpy.ndarray, scipy.sparse matrix

Symmetric positive-definite matrix (positive-definite if p is non-positive). Matrix can be dense or sparse.

Warning

Symmetry and positive (semi-) definiteness of A will not be checked. Make sure A satisfies these conditions.

Bnumpy.ndarray, scipy.sparse matrix, default=None

Symmetric positive-definite matrix (positive-definite if p is non-positive). Matrix can be dense or sparse. B should have the same size and type of A. If B is None (default value), it is assumed that B is the identity matrix.

Warning

Symmetry and positive (semi-) definiteness of B will not be checked. Make sure B satisfies these conditions.

pfloat, default=2

The order \(p\) in the Schatten \(p\)-norm which can be real positive, negative or zero.

optionsdict, default={}

At each interpolation point \(t_i\), the Schatten norm is computed using imate.schatten() function which itself calls either of

The options passes a dictionary of arguments to the above functions.

verbosebool, default=False

If True, it prints some information about the computation process.

tifloat or array_like(float), default=None

Interpolation points, which can be a single number, a list or an array of interpolation points. The interpolator honors the exact function values at the interpolant points.

func_type: {1, 2}, default=1

Type of interpolation function model. See Notes below for details.

See also

imate.InterpolateTrace
imate.InterpolateLogdet
imate.schatten

Notes

Schatten Norm:

In this class, the Schatten \(p\)-norm of the matrix \(\mathbf{A}\) is defined by

(1)#\[\begin{split}\Vert \mathbf{A} \Vert_p = \begin{cases} \left| \mathrm{det}(\mathbf{A}) \right|^{\frac{1}{n}}, & p=0, \\ \left| \frac{1}{n} \mathrm{trace}(\mathbf{A}^{p}) \right|^{\frac{1}{p}}, & p \neq 0, \end{cases}\end{split}\]

where \(n\) is the size of the matrix. When \(p \geq 0\), the above definition is the Schatten norm, and when \(p < 0\), the above is the Schatten anti-norm.

Note

Conventionally, the Schatten norm is defined without the normalizing factor \(\frac{1}{n}\) in (1). However, this factor is justified by the continuity granted by

(2)#\[\lim_{p \to 0} \Vert \mathbf{A} \Vert_p = \Vert \mathbf{A} \Vert_0.\]

See [1] (Section 2) and the examples in imate.schatten() for details.

Interpolation of Affine Matrix Function:

This class interpolates the one-parameter matrix function:

\[t \mapsto \| \mathbf{A} + t \mathbf{B} \|_p,\]

where the matrices \(\mathbf{A}\) and \(\mathbf{B}\) are symmetric and positive semi-definite (positive-definite if \(p < 0\)) and \(t \in [t_{\inf}, \infty)\) is a real parameter where \(t_{\inf}\) is the minimum \(t\) such that \(\mathbf{A} + t_{\inf} \mathbf{B}\) remains positive-definite.

Method of Interpolation:

Define the function

\[\tau_p(t) = \frac{\Vert \mathbf{A} + t \mathbf{B} \Vert_p} {\Vert \mathbf{B} \Vert_p},\]

and \(\tau_{p, 0} = \tau_p(0)\). Then, we approximate \(\tau_p(t)\) as follows. Transform the data \((t, \tau_p)\) to \((x, y)\) where

\[x = \log t\]

Also, if func_type=1, then \(y\) is defined by

\[y = \tau_p(t) - \tau_{p, 0} - t\]

and, if func_type=2, then \(y\) is defined by

\[y = \frac{\tau_p(t)}{\tau_{p, 0} + t} - 1.\]

The radial basis function method interpolates the data \((x, y)\) as follows:

  • If func_type is 1, cubic spline is used on to interpolate the data.

  • If func_type is 2, Gaussian radial basis functions is used to interpolate the data.

Boundary Conditions:

The following boundary conditions are added to the data \((x, y)\):

  • If func_type is 1, then the first and second derivative of the curve \(y(x)\) at \(x=0\) is set to zero.

  • If func_type is 2, then the function and \(y(x)\) and its first derivative at both ends \(x=0\) and \(x=1\) are extended to zero outside of the interval.

Interpolation Points:

The best practice is to provide an array of interpolation points that are equally distanced on the logarithmic scale. For instance, to produce four interpolation points in the interval \([10^{-2}, 1]\):

>>> import numpy
>>> ti = numpy.logspace(-2, 1, 4)

References

[1]

Ameli, S., and Shadden. S. C. (2022). Interpolating Log-Determinant and Trace of the Powers of Matrix \(\mathbf{A} + t \mathbf{B}\). Statistics and Computing 32, 108. https://doi.org/10.1007/s11222-022-10173-4.

Examples

Basic Usage:

Interpolate the Schatten 2-norm of the affine matrix function \(\mathbf{A} + t \mathbf{B}\) using rbf algorithm and the interpolating points \(t_i = [10^{-2}, 10^{-1}, 1, 10]\).

>>> # Generate two sample matrices (symmetric and positive-definite)
>>> from imate.sample_matrices import correlation_matrix
>>> A = correlation_matrix(size=20, scale=1e-1)
>>> B = correlation_matrix(size=20, scale=2e-2)

>>> # Initialize interpolator object
>>> from imate import InterpolateSchatten
>>> ti = [1e-2, 1e-1, 1, 1e1]
>>> f = InterpolateSchatten(A, B, p=2, kind='rbf', ti=ti, func_type=2)

>>> # Interpolate at an inquiry point t = 0.4
>>> t = 4e-1
>>> f(t)
1.72855247806288

Alternatively, call imate.InterpolateSchatten.interpolate() to interpolate at points t:

>>> # This is the same as f(t)
>>> f.interpolate(t)
1.72855247806288

To evaluate the exact value of the Schatten norm at point t without interpolation, call imate.InterpolateSchatten.eval() function:

>>> # This evaluates the function value at t exactly (no interpolation)
>>> f.eval(t)
1.7374809371539666

It can be seen that the relative error of interpolation compared to the exact solution in the above is \(0.51 \%\) using only four interpolation points \(t_i\), which is a remarkable result.

Warning

Calling imate.InterpolateSchatten.eval() may take a longer time to compute as it computes the function exactly. Particularly, if t is a large array, it may take a very long time to return the exact values.

Passing Options:

The above examples, the internal computation is passed to imate.trace() function since \(p=2\) is positive. You can pass arguments to the latter function using options argument. To do so, create a dictionary with the keys as the name of the argument. For instance, to use imate.trace(method=’slq’) method with min_num_samples=20 and max_num_samples=100, create the following dictionary:

>>> # Specify arguments as a dictionary
>>> options = {
...     'method': 'slq',
...     'min_num_samples': 20,
...     'max_num_samples': 100
... }

>>> # Pass the options to the interpolator
>>> f = InterpolateSchatten(A, B, p=2, options=options, kind='rbf',
...                         ti=ti, func_type=2)
>>> f(t)
1.6981895865829681

You may get a different result than the above as the slq method is a randomized method.

Interpolate on Range of Points:

Once the interpolation object f in the above example is instantiated, calling imate.InterpolateSchatten.interpolate() on a list of inquiry points t has almost no computational cost. The next example inquires interpolation on 1000 points t:

Interpolate an array of inquiry points t_array:

>>> # Create an interpolator object again
>>> ti = 1e-1
>>> f = InterpolateSchatten(A, B, kind='rbf', ti=ti, func_type=2)

>>> # Interpolate at an array of points
>>> import numpy
>>> t_array = numpy.logspace(-2, 1, 1000)
>>> norm_array = f.interpolate(t_array)

Plotting Interpolation and Compare with Exact Solution:

To plot the interpolation results, call imate.InterpolateSchatten.plot() function. To compare with the true values (without interpolation), pass compare=True to the above function.

Warning

By setting compare to True, every point in the array t is evaluated both using interpolation and with the exact method (no interpolation). If the size of t is large, this may take a very long run time.

>>> f.plot(t_array, normalize=True, compare=True)
../_images/interpolate_schatten_rbf.png

From the error plot in the above, it can be seen that with only four interpolation points, the error of interpolation for a wide range of \(t\) is no more than \(0.6 \%\). Also, note that the error on the interpolant points \(t_i=[10^{-2}, 10^{-1}, 1, 10]\) is zero since the interpolation scheme honors the exact function value at the interpolation points.

Attributes:
kindstr

Method of interpolation. For this class, kind is spl.

verbosebool

Verbosity of the computation process

nint

Size of the matrix

qint

Number of interpolant points.

pfloat

Order of Schatten \(p\)-norm

Methods

__call__

See imate.InterpolateSchatten.__call__().

eval

See imate.InterpolateSchatten.eval().

interpolate

See imate.InterpolateSchatten.interpolate().

bound

See imate.InterpolateSchatten.bound().

upper_bound

See imate.InterpolateSchatten.upper_bound().

plot

See imate.InterpolateSchatten.plot().