matrix_root_diagonal¶

def matrix_root_diagonal(
    A: torch.Tensor,
    root: int,
    epsilon: float = 0.0,
    inverse: bool = True,
    exponent_multiplier: float = 1.0,
    return_full_matrix: bool = False
) -> torch.Tensor:

Computes the inverse root of a diagonal matrix by taking the inverse square root of the diagonal entries. This function can either manipulate the given tensor directly if it represents a diagonal of a matrix or extract the diagonal from a 2D tensor and then proceed with the computation.

Parameters¶

Parameter	Type	Default	Description
`A`	`torch.Tensor`		A tensor representing either the diagonal of a matrix or a full diagonal matrix.
`root`	`int`		The root of interest. Must be a natural number.
`epsilon`	`float`	`0.0`	A small value added to the diagonal to avoid numerical issues.
`inverse`	`bool`	`True`	Specifies whether to return the inverse root.
`exponent_multiplier`	`float`	`1.0`	Multiplier for the exponent, providing additional transformation control.
`return_full_matrix`	`bool`	`False`	If `True`, the result is a full matrix with the diagonal altered. Otherwise, only the diagonal is returned.

Returns¶

Name	Type	Description
`X`	`torch.Tensor`	The resulting tensor after computing the inverse root of the diagonal matrix.

Overview¶

The matrix_root_diagonal function is an essential utility for operations such as whitening a covariance matrix where the matrix root is needed. It supports both direct diagonal input and square matrices, giving it versatility for various use cases.

Architecture and Operation¶

The internal workflow checks the dimensionality of the input tensor A. It raises an exception for non-2D tensors. For input representing a full square matrix, it extracts the diagonal. The necessary inverse root computations are then applied to the diagonal entries, with an option to reintegrate them into a full matrix.

Usage Example 1: Basic Diagonal Tensor¶

import torch

from zeta.ops import matrix_root_diagonal

# Create a diagonal tensor
A = torch.tensor([4.0, 9.0, 16.0])

# Compute the inverse square root of the diagonal
root_matrix = matrix_root_diagonal(A, root=2)

print(root_matrix)

Usage Example 2: Full matrix with epsilon¶

import torch

from zeta.ops import matrix_root_diagonal

# Create a diagonal matrix
A = torch.diag(torch.tensor([4.0, 9.0, 16.0]))

# Compute the inverse square root of the diagonal with epsilon
root_matrix = matrix_root_diagonal(A, root=2, epsilon=0.1)

print(root_matrix)

Usage Example 3: Return Full Matrix¶

import torch

from zeta.ops import matrix_root_diagonal

# Create a diagonal tensor
A = torch.tensor([4.0, 9.0, 16.0])

# Compute the inverse square root and return the full matrix
root_matrix = matrix_root_diagonal(A, root=2, return_full_matrix=True)

print(root_matrix)

Additional Information & Tips¶

The function ensures numerical stability by adding a small value epsilon to the diagonal before computation.
The computation involves element-wise operations. Hence, the input tensor A is expected to have one or two dimensions only.
Setting inverse to False results in the computation of the direct root rather than the inverse.

References and Further Reading¶

For a better understanding of matrix roots and their applications, the following resources may be helpful: - Higham, Nicholas J. "Computing real square roots of a real matrix." Linear Algebra and its applications 88 (1987): 405-430. - Wikipedia entry on Matrix Functions: https://en.wikipedia.org/wiki/Matrix_function