Add cholesky_decomposition.py

DIRECTORY.md

@@ -720,6 +720,7 @@
   * [Chebyshev Distance](maths/chebyshev_distance.py)
   * [Check Polygon](maths/check_polygon.py)
   * [Chinese Remainder Theorem](maths/chinese_remainder_theorem.py)
+  * [Cholesky Decomposition](maths/cholesky_decomposition.py)
   * [Chudnovsky Algorithm](maths/chudnovsky_algorithm.py)
   * [Collatz Sequence](maths/collatz_sequence.py)
   * [Combinations](maths/combinations.py)

maths/cholesky_decomposition.py

@@ -0,0 +1,120 @@
+import numpy as np
+
+
+def cholesky_decomposition(matrix: np.ndarray) -> np.ndarray:
+    """Return a Cholesky decomposition of the matrix A.
+
+    The Cholesky decomposition decomposes the square, positive definite matrix A
+    into a lower triangular matrix L such that A = L L^T.
+
+    https://en.wikipedia.org/wiki/Cholesky_decomposition
+
+    Arguments:
+    A -- a numpy.ndarray of shape (n, n)
+
+    >>> A = np.array([[4, 12, -16], [12, 37, -43], [-16, -43, 98]], dtype=float)
+    >>> L = cholesky_decomposition(A)
+    >>> np.allclose(L, np.array([[2, 0, 0], [6, 1, 0], [-8, 5, 3]]))
+    True
+
+    >>> # check that the decomposition is correct
+    >>> np.allclose(L @ L.T, A)
+    True
+
+    >>> # check that L is lower triangular
+    >>> np.allclose(np.tril(L), L)
+    True
+
+    The Cholesky decomposition can be used to solve the linear system A x = y.
+
+    >>> x_true = np.array([1, 2, 3], dtype=float)
+    >>> y = A @ x_true
+    >>> x = solve_cholesky(L, y)
+    >>> np.allclose(x, x_true)
+    True
+
+    It can also be used to solve multiple equations A X = Y simultaneously.
+
+    >>> X_true = np.random.rand(3, 3)
+    >>> Y = A @ X_true
+    >>> X = solve_cholesky(L, Y)
+    >>> np.allclose(X, X_true)
+    True
+    """
+
+    assert matrix.shape[0] == matrix.shape[1], (
+        f"Input matrix is not square, {matrix.shape=}"
+    )
+    assert np.allclose(matrix, matrix.T), "Input matrix must be symmetric"
+
+    n = matrix.shape[0]
+    lower_triangle = np.tril(matrix)
+
+    for i in range(n):
+        for j in range(i + 1):
+            lower_triangle[i, j] -= np.sum(
+                lower_triangle[i, :j] * lower_triangle[j, :j]
+            )
+
+            if i == j:
+                if lower_triangle[i, i] <= 0:
+                    raise ValueError("Matrix A is not positive definite")
+
+                lower_triangle[i, i] = np.sqrt(lower_triangle[i, i])
+            else:
+                lower_triangle[i, j] /= lower_triangle[j, j]
+
+    return lower_triangle
+
+
+def solve_cholesky(
+    lower_triangle: np.ndarray,
+    right_hand_side: np.ndarray,
+) -> np.ndarray:
+    """Given a Cholesky decomposition L L^T = A of a matrix A, solve the
+    system of equations A X = Y where the right-hand side Y is either
+    a matrix or a vector.
+
+    >>> L = np.array([[2, 0], [3, 4]], dtype=float)
+    >>> Y = np.array([[22, 54], [81, 193]], dtype=float)
+    >>> X = solve_cholesky(L, Y)
+    >>> np.allclose(X, np.array([[1, 3], [3, 7]], dtype=float))
+    True
+    """
+
+    assert lower_triangle.shape[0] == lower_triangle.shape[1], (
+        f"Matrix L is not square, {lower_triangle.shape=}"
+    )
+    assert np.allclose(np.tril(lower_triangle), lower_triangle), (
+        "Matrix L is not lower triangular"
+    )
+
+    # Handle vector case by reshaping to matrix and then flattening again
+    if len(right_hand_side.shape) == 1:
+        return solve_cholesky(lower_triangle, right_hand_side.reshape(-1, 1)).ravel()
+
+    n = right_hand_side.shape[0]
+
+    # Solve L W = Y for W
+    w = right_hand_side.copy()
+    for i in range(n):
+        for j in range(i):
+            w[i] -= lower_triangle[i, j] * w[j]
+
+        w[i] /= lower_triangle[i, i]
+
+    # Solve L^T X = W for X
+    x = w
+    for i in reversed(range(n)):
+        for j in range(i + 1, n):
+            x[i] -= lower_triangle[j, i] * x[j]
+
+        x[i] /= lower_triangle[i, i]
+
+    return x
+
+
+if __name__ == "__main__":
+    import doctest
+
+    doctest.testmod()