**π‘ Problem Formulation:** In the realm of linear algebra, computing the inverse of an n-dimensional array (typically a matrix) is a fundamental operation, enabling solutions to linear equations and transformations. In Python, this task can be performed using various methods, each suited to different scenarios and array types. For our purposes, we assume that the input is a square n x n array for which an inverse exists. The expected output is another n x n array representing the inverse of the input array.

## Method 1: Using NumPy’s `inv`

function

The NumPy library’s `inv`

function provides an efficient and widely-used approach to compute the inverse of an n-dimensional array. NumPy is optimized for numerical computations and leverages highly optimized C and Fortran code under the hood. The `numpy.linalg.inv()`

function specifically computes the multiplicative inverse of a matrix.

Here’s an example:

import numpy as np # Define a 2x2 array A = np.array([[3, 2], [1, 4]]) # Compute the inverse A_inv = np.linalg.inv(A) print(A_inv)

The output of this code snippet:

[[ 0.4 -0.2] [-0.1 0.3]]

This code snippet first imports the NumPy library, then defines a 2×2 matrix `A`

. The function `np.linalg.inv()`

is used to calculate the inverse of this matrix, and the result `A_inv`

is printed. The output shows the computed inverse of the original matrix.

## Method 2: Using SymPy’s `inv`

Method

SymPy is a Python library for symbolic mathematics. It can compute the inverse of a matrix exactly, without numerical approximation. The method `Matrix.inv()`

from SymPy takes a matrix and returns its inverse if it exists. This can be particularly useful when working with symbolic variables or when exact results are needed.

Here’s an example:

from sympy import Matrix # Define a 2x2 matrix with symbols A = Matrix([[3, 2], [1, 4]]) # Compute the inverse A_inv = A.inv() print(A_inv)

The output of this code snippet:

Matrix([ [ 2/5, -1/5], [-1/10, 3/10]])

This example uses SymPy to define a 2×2 symbolic matrix and then calculates its inverse using the `inv()`

method. The result is printed as an exact fraction rather than a decimal, showcasing the benefit of using a symbolic computation library for this task.

## Method 3: Using scipy.linalg’s `inv`

Function

The SciPy library, which extends NumPy, offers additional functionality for scientific computing. The `scipy.linalg.inv()`

function provides an alternative way to compute the inverse of a matrix. While similar to NumPy’s inverse method, SciPy’s version sometimes offers performance improvements due to its specific optimization.

Here’s an example:

from scipy.linalg import inv # Define a 2x2 array A = np.array([[3, 2], [1, 4]]) # Compute the inverse using SciPy A_inv = inv(A) print(A_inv)

The output of this code snippet:

[[ 0.4 -0.2] [-0.1 0.3]]

Here, the `inv`

function from the SciPy library’s `linalg`

(linear algebra) module is used to compute the inverse of a defined 2×2 array. The output mirrors the result obtained with NumPy, displaying its effectiveness in such computations.

## Method 4: Using the Gaussian Elimination Technique

The Gaussian elimination method, or row reduction, is a classic algorithm in linear algebra for solving systems of linear equations. It can also be used to compute the inverse of a matrix by augmenting the original matrix with the identity matrix and performing row operations. Implementation is more complex and is typically used for educational purposes rather than in production code due to the existence of optimized libraries.

Here’s an example:

# A manual implementation would be needed here. Omitted for brevity.

This method, while fundamental in understanding matrix inversions, is less practical for coding purposes, hence more theoretical and not provided in full detail here.

## Bonus One-Liner Method 5: Using NumPy’s `pinv`

for Pseudo-Inverse

Sometimes a matrix may not have an exact inverse, particularly if it is not square or if it is singular. For these cases, the pseudo-inverse, computed with the `np.linalg.pinv()`

function, provides a best-fit approximation that minimizes the least squares error.

Here’s an example:

import numpy as np # Define a non-square or singular 2x3 array A = np.array([[3, 2, 1], [1, 4, 5]]) # Compute the pseudo-inverse A_pinv = np.linalg.pinv(A) print(A_pinv)

The output of this code snippet:

# The output will be a 3x2 matrix representing the pseudo-inverse of A.

This concise method utilizes NumPy’s `pinv`

to calculate a pseudo-inverse for arrays that cannot be inverted using the standard inverse function, offering a practical alternative for these special cases.

## Summary/Discussion

**Method 1: NumPy inv Function.**Strengths: Efficient, simple to use, well-optimized. Weaknesses: Depends on the NumPy library, unsuitable for symbolic calculations.**Method 2: SymPy inv Method.**Strengths: Provides exact results, great for symbolic math. Weaknesses: May not be as efficient as numerical methods, requires SymPy package.**Method 3: scipy.linalg inv Function.**Strengths: Offers potential performance improvements over NumPy. Weaknesses: Requires additional SciPy library, similar to NumPy’s functionality.**Method 4: Gaussian Elimination Technique.**Strengths: Educational tool, no library dependencies. Weaknesses: Computationally intensive, manual implementation complexity.**Method 5: NumPy pinv for Pseudo-Inverse.**Strengths: Provides solutions for non-square or singular matrices. Weaknesses: May not be suitable where an exact inverse is required.