.gitignore

@@ -1 +1,2 @@
-build/
+build/
+venv/

qr-decom.py

@@ -0,0 +1,46 @@
+import numpy as np
+
+def householder_reflection(A):
+    """
+    Perform QR decomposition using Householder reflection.
+    
+    Arguments:
+    A -- A matrix to be decomposed (m x n).
+    
+    Returns:
+    Q -- Orthogonal matrix (m x m).
+    R -- Upper triangular matrix (m x n).
+    """
+    A = A.astype(float)  # Ensure the matrix is of type float
+    m, n = A.shape
+    Q = np.eye(m)  # Initialize Q as an identity matrix
+    R = A.copy()  # R starts as a copy of A
+
+    # Apply Householder reflections for each column
+    for k in range(n):
+        # Step 1: Compute the Householder vector
+        x = R[k:m, k]
+        e1 = np.zeros_like(x)
+        e1[0] = np.linalg.norm(x) if x[0] >= 0 else -np.linalg.norm(x)
+        v = x + e1
+        v = v / np.linalg.norm(v)  # Normalize v
+        
+        # Step 2: Apply the reflection to the matrix
+        R[k:m, k:n] = R[k:m, k:n] - 2 * np.outer(v, v.T @ R[k:m, k:n])
+        
+        # Step 3: Apply the reflection to Q
+        Q[:, k:m] = Q[:, k:m] - 2 * np.outer(Q[:, k:m] @ v, v)
+
+    # The resulting Q and R are the QR decomposition
+    return Q, R
+
+# Example usage
+A = np.array([[12, -51, 4], 
+              [6, 167, -68], 
+              [-4, 24, -41]])
+
+Q, R = householder_reflection(A)
+print("Q matrix:")
+print(Q)
+print("\nR matrix:")
+print(R)