diff --git a/qr-decom.py b/qr-decom.py
index 774cac7..6a1dd8b 100644
--- a/qr-decom.py
+++ b/qr-decom.py
@@ -44,3 +44,62 @@ print("Q matrix:")
 print(Q)
 print("\nR matrix:")
 print(R)
+print("Multiplied Together:")
+print(Q@R)
+
+def svd_decomposition(A):
+    """
+    Perform Singular Value Decomposition (SVD) from scratch.
+    
+    Arguments:
+    A -- The matrix to be decomposed (m x n).
+    
+    Returns:
+    U -- Orthogonal matrix of left singular vectors (m x m).
+    Sigma -- Diagonal matrix of singular values (m x n).
+    Vt -- Orthogonal matrix of right singular vectors (n x n).
+    """
+    # Step 1: Compute A^T A
+    AtA = np.dot(A.T, A)  # A transpose multiplied by A
+    
+    # Step 2: Compute the eigenvalues and eigenvectors of A^T A
+    eigenvalues, V = np.linalg.eig(AtA)
+    
+    # Step 3: Sort eigenvalues in descending order and sort V accordingly
+    sorted_indices = np.argsort(eigenvalues)[::-1]  # Indices to sort eigenvalues in descending order
+    eigenvalues = eigenvalues[sorted_indices]
+    V = V[:, sorted_indices]
+    
+    # Step 4: Compute the singular values (sqrt of eigenvalues)
+    singular_values = np.sqrt(eigenvalues)
+    
+    # Step 5: Construct the Sigma matrix
+    m, n = A.shape
+    Sigma = np.zeros((m, n))  # Initialize Sigma as a zero matrix
+    for i in range(min(m, n)):
+        Sigma[i, i] = singular_values[i]  # Place the singular values on the diagonal
+    
+    # Step 6: Compute the U matrix using A * V = U * Sigma
+    U = np.dot(A, V)  # A * V gives us the unnormalized U
+    # Normalize the columns of U
+    for i in range(U.shape[1]):
+        U[:, i] = U[:, i] / singular_values[i]  # Normalize each column by the corresponding singular value
+    
+    # Step 7: Return U, Sigma, Vt
+    return U, Sigma, V.T  # V.T is the transpose of V
+
+# Example usage
+A = np.array([[12, -51, 4],
+              [6, 167, -68],
+              [-4, 24, -41]])
+
+U, Sigma, Vt = svd_decomposition(A)
+
+print("\nSVD DECOMPOSITION\nU matrix:")
+print(U)
+print("\nSigma matrix:")
+print(Sigma)
+print("\nVt matrix:")
+print(Vt)
+print("Multiplied together:")
+print(U@Sigma@Vt)