Delete arithmetic_analysis/ directory and relocate its contents (#10824)

* Remove eval from arithmetic_analysis/newton_raphson.py

* Relocate contents of arithmetic_analysis/

Delete the arithmetic_analysis/ directory and relocate its files because
the purpose of the directory was always ill-defined. "Arithmetic
analysis" isn't a field of math, and the directory's files contained
algorithms for linear algebra, numerical analysis, and physics.

Relocated the directory's linear algebra algorithms to linear_algebra/,
its numerical analysis algorithms to a new subdirectory called
maths/numerical_analysis/, and its single physics algorithm to physics/.

* updating DIRECTORY.md

---------

Co-authored-by: github-actions <${GITHUB_ACTOR}@users.noreply.github.com>

linear_algebra/gaussian_elimination.py

@@ -0,0 +1,86 @@
+"""
+Gaussian elimination method for solving a system of linear equations.
+Gaussian elimination - https://en.wikipedia.org/wiki/Gaussian_elimination
+"""
+
+
+import numpy as np
+from numpy import float64
+from numpy.typing import NDArray
+
+
+def retroactive_resolution(
+    coefficients: NDArray[float64], vector: NDArray[float64]
+) -> NDArray[float64]:
+    """
+    This function performs a retroactive linear system resolution
+        for triangular matrix
+
+    Examples:
+        2x1 + 2x2 - 1x3 = 5         2x1 + 2x2 = -1
+        0x1 - 2x2 - 1x3 = -7        0x1 - 2x2 = -1
+        0x1 + 0x2 + 5x3 = 15
+    >>> gaussian_elimination([[2, 2, -1], [0, -2, -1], [0, 0, 5]], [[5], [-7], [15]])
+    array([[2.],
+           [2.],
+           [3.]])
+    >>> gaussian_elimination([[2, 2], [0, -2]], [[-1], [-1]])
+    array([[-1. ],
+           [ 0.5]])
+    """
+
+    rows, columns = np.shape(coefficients)
+
+    x: NDArray[float64] = np.zeros((rows, 1), dtype=float)
+    for row in reversed(range(rows)):
+        total = np.dot(coefficients[row, row + 1 :], x[row + 1 :])
+        x[row, 0] = (vector[row][0] - total[0]) / coefficients[row, row]
+
+    return x
+
+
+def gaussian_elimination(
+    coefficients: NDArray[float64], vector: NDArray[float64]
+) -> NDArray[float64]:
+    """
+    This function performs Gaussian elimination method
+
+    Examples:
+        1x1 - 4x2 - 2x3 = -2        1x1 + 2x2 = 5
+        5x1 + 2x2 - 2x3 = -3        5x1 + 2x2 = 5
+        1x1 - 1x2 + 0x3 = 4
+    >>> gaussian_elimination([[1, -4, -2], [5, 2, -2], [1, -1, 0]], [[-2], [-3], [4]])
+    array([[ 2.3 ],
+           [-1.7 ],
+           [ 5.55]])
+    >>> gaussian_elimination([[1, 2], [5, 2]], [[5], [5]])
+    array([[0. ],
+           [2.5]])
+    """
+    # coefficients must to be a square matrix so we need to check first
+    rows, columns = np.shape(coefficients)
+    if rows != columns:
+        return np.array((), dtype=float)
+
+    # augmented matrix
+    augmented_mat: NDArray[float64] = np.concatenate((coefficients, vector), axis=1)
+    augmented_mat = augmented_mat.astype("float64")
+
+    # scale the matrix leaving it triangular
+    for row in range(rows - 1):
+        pivot = augmented_mat[row, row]
+        for col in range(row + 1, columns):
+            factor = augmented_mat[col, row] / pivot
+            augmented_mat[col, :] -= factor * augmented_mat[row, :]
+
+    x = retroactive_resolution(
+        augmented_mat[:, 0:columns], augmented_mat[:, columns : columns + 1]
+    )
+
+    return x
+
+
+if __name__ == "__main__":
+    import doctest
+
+    doctest.testmod()