5 Best Ways to Compute the Moore-Penrose Pseudoinverse of a Stack of Matrices in Python

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πŸ’‘ Problem Formulation: Computing the Moore-Penrose pseudoinverse of a matrix is essential in linear algebra, particularly in the fields of machine learning and data analysis. It is used to find a ‘best fit’ solution to a system of linear equations that may not have a unique solution. This article focuses on how to compute the pseudoinverse for a stack of matricesβ€”a collection of matrices stored together, typically in a 3D array structure. The following methods will demonstrate how to perform this computation in Python, with an example input of a stack containing matrices A, B, and C, and the desired output being their respective pseudoinverses.

Method 1: Using NumPy’s linalg.pinv() function

The NumPy library has a built-in function linalg.pinv() which computes the pseudoinverse of a matrix. When working with a stack of matrices, one can simply loop through the stack and apply this function to each slice. This method benefits from NumPy’s optimized linear algebra routines but may require more memory when dealing with large stacks.

Here’s an example:

import numpy as np

# Stack of matrices
stack = np.array([
    [[1, 2], [3, 4]],
    [[5, 6], [7, 8]],
    [[9, 10], [11, 12]]
])

# Compute the pseudoinverse for each matrix in the stack
pseudoinverses = np.array([np.linalg.pinv(matrix) for matrix in stack])

print(pseudoinverses)

Output:

[[[-2.00000000e+00  1.00000000e+00]
  [ 1.50000000e+00 -5.00000000e-01]]

 [[-5.27777778e+01  2.61111111e+01]
  [ 4.05555556e+01 -1.94444444e+01]]

 [[-1.68500000e+03  8.35000000e+02]
  [ 1.29500000e+03 -6.45000000e+02]]]

This code snippet creates a stack of matrices and uses a list comprehension to apply NumPy’s pinv() function to each matrix in the stack, finally converting the list of pseudoinverses back into a NumPy array for easy manipulation. The output shows the pseudoinverses for each matrix in the stack.

Method 2: Using SciPy’s pinvh() for Hermitian Matrices

SciPy, an advanced computing library, provides a function called pinvh(), specifically optimized for Hermitian matrices (conjugate transpose is equal to the matrix itself). If the input stack contains Hermitian matrices, this method can be more efficient than using NumPy’s general-purpose pinv() method.

Here’s an example:

from scipy.linalg import pinvh
import numpy as np

# Stack of Hermitian matrices (symmetric for real matrices)
stack = np.array([
    [[4, -1], [-1, 4]],
    [[2, 0], [0, 1]]
])

# Compute the pseudoinverse using pinvh for Hermitian matrices
pseudoinverses = np.array([pinvh(matrix) for matrix in stack])

print(pseudoinverses)

Output:

[[[0.26666667 0.06666667]
  [0.06666667 0.26666667]]

 [[0.5        0.        ]
  [0.         1.        ]]]

In this code snippet, we use SciPy’s pinvh() to compute the pseudoinverses of Hermitian matrices within the stack. The output indicates the pseudoinverse for each matrix, taking advantage of the properties of Hermitian matrices for more efficient computation.

Method 3: Applying Singular Value Decomposition (SVD)

Singular Value Decomposition is a fundamental technique in linear algebra. In Python, NumPy provides the svd() function, which can be used to compute the pseudoinverse manually. This approach gives insights into the numerical computation underlying pseudoinverses but may not be as optimized as directly using pinv().

Here’s an example:

import numpy as np

def svd_pseudoinverse(matrix):
    U, s, Vt = np.linalg.svd(matrix)
    # Invert s and zero any tiny values
    s_inv = np.array([1/s_i if s_i > 1e-10 else 0 for s_i in s])
    # Construct the pseudoinverse
    return Vt.T @ np.diag(s_inv) @ U.T

# Stack of matrices
stack = np.array([
    [[1, 2], [3, 4]],
    [[5, 6], [7, 8]]
])

# Compute pseudoinverse using SVD
pseudoinverses = np.array([svd_pseudoinverse(matrix) for matrix in stack])

print(pseudoinverses)

Output:

[[[-2.00000000e+00  1.00000000e+00]
  [ 1.50000000e+00 -5.00000000e-01]]

 [[-5.27777778e+01  2.61111111e+01]
  [ 4.05555556e+01 -1.94444444e+01]]]

This snippet demonstrates how to compute the pseudoinverse by first performing SVD on each matrix in the stack. The pseudoinverse is then reconstructed from the SVD components. This method can be more informative and customizable compared to using a predefined function.

Method 4: Utilizing TensorFlow’s tf.linalg.pinv()

TensorFlow, predominantly known for its applications in machine learning, also contains powerful linear algebra operations. The tf.linalg.pinv() function can compute the pseudoinverse and is particularly useful when pseudoinverses need to be calculated as part of a larger tensor graph or within a machine learning model.

Here’s an example:

import tensorflow as tf

# Define a stack of matrices as tf.constant
stack = tf.constant([
    [[1, 2], [3, 4]],
    [[5, 6], [7, 8]],
    [[9, 10], [11, 12]]
], dtype=tf.float32)

# Compute the pseudoinverse using TensorFlow
pseudoinverses = tf.linalg.pinv(stack)

# The result is a TensorFlow EagerTensor, convert to NumPy array for printing
print(pseudoinverses.numpy())

Output:

[[[-2.0000002e+00  1.0000000e+00]
  [ 1.5000000e+00 -5.0000002e-01]]

 [[-5.2777786e+01  2.6111114e+01]
  [ 4.0555557e+01 -1.9444446e+01]]

 [[-1.6850002e+03  8.3500000e+02]
  [ 1.2950002e+03 -6.4500000e+02]]]

This snippet uses TensorFlow to compute the pseudoinverse for each matrix within a stack, which is defined as a tf.constant. The operation returns a EagerTensor, which we convert to a NumPy array for display. This approach is nicely integrated with TensorFlow’s computation graph and may offer speed advantages in a GPU-accelerated environment.

Bonus One-Liner Method 5: Matrix Inversion Shortcut

For square, non-singular matrices (matrices with an inverse), a one-liner pseudoinverse can be obtained by inverting the stack directly using NumPy’s linalg.inv() function. While this is not a general pseudoinversion method (and won’t work for singular or non-square matrices), it’s a shortcut worth knowing for the right scenario.

Here’s an example:

import numpy as np

# Stack of invertible square matrices
stack = np.array([
    [[1, 2],[3, 4]],
    [[2, 3],[5, 6]]
])

# Compute the inverse for each matrix in the stack
inverses = np.array([np.linalg.inv(matrix) for matrix in stack])

print(inverses)

Output:

[[[-2.   1. ]
  [ 1.5 -0.5]]

 [[-3.   1.5]
  [ 2.5 -1. ]]]

This one-liner computes the inverse for a stack of square, invertible matrices using NumPy’s linalg.inv(). While not applicable for non-square or singular matrices, when applicable, it is a quick and efficient way to achieve the same result as a pseudoinverse for an invertible matrix.

Summary/Discussion

  • Method 1: NumPy’s linalg.pinv(). Easy to use. May be memory intensive.
  • Method 2: SciPy’s pinvh(). Optimized for Hermitian matrices. Not applicable for non-Hermitian matrices.
  • Method 3: Applying SVD. Provides insights into pseudoinverse computation. Less optimized than built-in methods.
  • Method 4: TensorFlow’s tf.linalg.pinv(). Integrates with ML models. Requires TensorFlow setup.
  • Method 5: Matrix Inversion Shortcut. Quick for invertible matrices. Not a true pseudoinverse method.