Rename variables

maths/cholesky_decomposition.py

@@ -1,8 +1,7 @@
 import numpy as np
 
 
-# ruff: noqa: N803,N806
-def cholesky_decomposition(A: np.ndarray) -> np.ndarray:
+def cholesky_decomposition(a: np.ndarray) -> np.ndarray:
     """Return a Cholesky decomposition of the matrix A.
 
     The Cholesky decomposition decomposes the square, positive definite matrix A
@@ -26,7 +25,7 @@ def cholesky_decomposition(A: np.ndarray) -> np.ndarray:
     >>> np.allclose(np.tril(L), L)
     True
 
-    The Cholesky decomposition can be used to solve the system of equations A x = y.
+    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
@@ -43,28 +42,30 @@ def cholesky_decomposition(A: np.ndarray) -> np.ndarray:
     True
     """
 
-    assert A.shape[0] == A.shape[1], f"Matrix A is not square, {A.shape=}"
-    assert np.allclose(A, A.T), "Matrix A must be symmetric"
+    assert a.shape[0] == a.shape[1], f"Matrix A is not square, {a.shape=}"
+    assert np.allclose(a, a.T), "Matrix A must be symmetric"
 
-    n = A.shape[0]
-    L = np.tril(A)
+    n = a.shape[0]
+    lower_triangle = np.tril(a)
 
     for i in range(n):
         for j in range(i + 1):
-            L[i, j] -= np.sum(L[i, :j] * L[j, :j])
+            lower_triangle[i, j] -= np.sum(
+                lower_triangle[i, :j] * lower_triangle[j, :j]
+            )
 
             if i == j:
-                if L[i, i] <= 0:
+                if lower_triangle[i, i] <= 0:
                     raise ValueError("Matrix A is not positive definite")
 
-                L[i, i] = np.sqrt(L[i, i])
+                lower_triangle[i, i] = np.sqrt(lower_triangle[i, i])
             else:
-                L[i, j] /= L[j, j]
+                lower_triangle[i, j] /= lower_triangle[j, j]
 
-    return L
+    return lower_triangle
 
 
-def solve_cholesky(L: np.ndarray, Y: np.ndarray) -> np.ndarray:
+def solve_cholesky(lower_triangle: np.ndarray, y: 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 Y is either a matrix or a vector.
 
@@ -75,32 +76,36 @@ def solve_cholesky(L: np.ndarray, Y: np.ndarray) -> np.ndarray:
     True
     """
 
-    assert L.shape[0] == L.shape[1], f"Matrix L is not square, {L.shape=}"
-    assert np.allclose(np.tril(L), L), "Matrix L is not lower triangular"
+    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(Y.shape) == 1:
-        return solve_cholesky(L, Y.reshape(-1, 1)).ravel()
+    if len(y.shape) == 1:
+        return solve_cholesky(lower_triangle, y.reshape(-1, 1)).ravel()
 
-    n = Y.shape[0]
+    n = y.shape[0]
 
     # Solve L W = B for W
-    W = Y.copy()
+    w = y.copy()
     for i in range(n):
         for j in range(i):
-            W[i] -= L[i, j] * W[j]
+            w[i] -= lower_triangle[i, j] * w[j]
 
-        W[i] /= L[i, i]
+        w[i] /= lower_triangle[i, i]
 
     # Solve L^T X = W for X
-    X = W
+    x = w
     for i in reversed(range(n)):
         for j in range(i + 1, n):
-            X[i] -= L[j, i] * X[j]
+            x[i] -= lower_triangle[j, i] * x[j]
 
-        X[i] /= L[i, i]
+        x[i] /= lower_triangle[i, i]
 
-    return X
+    return x
 
 
 if __name__ == "__main__":