Updating power_iteration.py

Key Changes:
Type Hints: Updated to include npt.NDArray for better type checking with numpy arrays and Union for real/complex compatibility.
Error Handling: More informative assertion errors for matrix dimensions and types.
Matrix Shape Handling: Explicitly named dimensions (N, M) to ensure clarity.
Complex Hermitian Check: Uses assert with an error message for better debugging.

Author: da-ksh-esh

linear_algebra/src/power_iteration.py

@@ -1,80 +1,78 @@
 import numpy as np
-
+from typing import Tuple, Union
+import numpy.typing as npt
 
 def power_iteration(
-    input_matrix: np.ndarray,
-    vector: np.ndarray,
+    input_matrix: npt.NDArray[Union[np.float64, np.complex128]],
+    vector: npt.NDArray[Union[np.float64, np.complex128]],
     error_tol: float = 1e-12,
     max_iterations: int = 100,
-) -> tuple[float, np.ndarray]:
+) -> Tuple[float, npt.NDArray[Union[np.float64, np.complex128]]]:
     """
     Power Iteration.
     Find the largest eigenvalue and corresponding eigenvector
     of matrix input_matrix given a random vector in the same space.
-    Will work so long as vector has component of largest eigenvector.
+    Works as long as vector has a component of the largest eigenvector.
     input_matrix must be either real or Hermitian.
 
-    Input
-    input_matrix: input matrix whose largest eigenvalue we will find.
-    Numpy array. np.shape(input_matrix) == (N,N).
-    vector: random initial vector in same space as matrix.
-    Numpy array. np.shape(vector) == (N,) or (N,1)
+    Input:
+    input_matrix: Input matrix whose largest eigenvalue we will find.
+    A square numpy array. Shape: (N, N).
+    vector: Random initial vector in the same space as the matrix.
+    Numpy array. Shape: (N,) or (N,1).
 
-    Output
-    largest_eigenvalue: largest eigenvalue of the matrix input_matrix.
-    Float. Scalar.
-    largest_eigenvector: eigenvector corresponding to largest_eigenvalue.
-    Numpy array. np.shape(largest_eigenvector) == (N,) or (N,1).
+    Output:
+    largest_eigenvalue: Largest eigenvalue of the matrix.
+    largest_eigenvector: Eigenvector corresponding to largest eigenvalue.
 
+    Example:
     >>> import numpy as np
     >>> input_matrix = np.array([
-    ... [41,  4, 20],
-    ... [ 4, 26, 30],
+    ... [41, 4, 20],
+    ... [4, 26, 30],
     ... [20, 30, 50]
     ... ])
-    >>> vector = np.array([41,4,20])
-    >>> power_iteration(input_matrix,vector)
+    >>> vector = np.array([41, 4, 20])
+    >>> power_iteration(input_matrix, vector)
     (79.66086378788381, array([0.44472726, 0.46209842, 0.76725662]))
     """
-
     # Ensure matrix is square.
-    assert np.shape(input_matrix)[0] == np.shape(input_matrix)[1]
+    N, M = np.shape(input_matrix)
+    assert N == M, "Input matrix must be square."
     # Ensure proper dimensionality.
-    assert np.shape(input_matrix)[0] == np.shape(vector)[0]
+    assert N == np.shape(vector)[0], "Vector must be compatible with matrix dimensions."
     # Ensure inputs are either both complex or both real
-    assert np.iscomplexobj(input_matrix) == np.iscomplexobj(vector)
+    assert np.iscomplexobj(input_matrix) == np.iscomplexobj(vector), "Both inputs must be either real or complex."
+    
     is_complex = np.iscomplexobj(input_matrix)
     if is_complex:
-        # Ensure complex input_matrix is Hermitian
-        assert np.array_equal(input_matrix, input_matrix.conj().T)
-
-    # Set convergence to False. Will define convergence when we exceed max_iterations
-    # or when we have small changes from one iteration to next.
+        # Ensure complex input_matrix is Hermitian (A == A*)
+        assert np.array_equal(input_matrix, input_matrix.conj().T), "Input matrix must be Hermitian if complex."
 
     convergence = False
-    lambda_previous = 0
+    lambda_previous = 0.0
     iterations = 0
-    error = 1e12
+    error = float("inf")
 
     while not convergence:
-        # Multiple matrix by the vector.
+        # Multiply matrix by the vector.
         w = np.dot(input_matrix, vector)
         # Normalize the resulting output vector.
         vector = w / np.linalg.norm(w)
-        # Find rayleigh quotient
-        # (faster than usual b/c we know vector is normalized already)
+        # Find the Rayleigh quotient (faster since we know vector is normalized).
         vector_h = vector.conj().T if is_complex else vector.T
         lambda_ = np.dot(vector_h, np.dot(input_matrix, vector))
 
         # Check convergence.
-        error = np.abs(lambda_ - lambda_previous) / lambda_
+        error = np.abs(lambda_ - lambda_previous) / np.abs(lambda_)
         iterations += 1
 
         if error <= error_tol or iterations >= max_iterations:
             convergence = True
 
         lambda_previous = lambda_
 
+    # Ensure lambda_ is real if the matrix is complex.
     if is_complex:
         lambda_ = np.real(lambda_)
 
@@ -83,7 +81,8 @@ def power_iteration(
 
 def test_power_iteration() -> None:
     """
-    >>> test_power_iteration()  # self running tests
+    Test function for power_iteration.
+    Runs tests on real and complex matrices using the power_iteration function.
     """
     real_input_matrix = np.array([[41, 4, 20], [4, 26, 30], [20, 30, 50]])
     real_vector = np.array([41, 4, 20])
@@ -105,19 +104,12 @@ def test_power_iteration() -> None:
         eigen_value, eigen_vector = power_iteration(input_matrix, vector)
 
         # Numpy implementation.
-
-        # Get eigenvalues and eigenvectors using built-in numpy
-        # eigh (eigh used for symmetric or hermetian matrices).
         eigen_values, eigen_vectors = np.linalg.eigh(input_matrix)
-        # Last eigenvalue is the maximum one.
         eigen_value_max = eigen_values[-1]
-        # Last column in this matrix is eigenvector corresponding to largest eigenvalue.
         eigen_vector_max = eigen_vectors[:, -1]
 
-        # Check our implementation and numpy gives close answers.
+        # Check that our implementation and numpy's give close answers.
         assert np.abs(eigen_value - eigen_value_max) <= 1e-6
-        # Take absolute values element wise of each eigenvector.
-        # as they are only unique to a minus sign.
         assert np.linalg.norm(np.abs(eigen_vector) - np.abs(eigen_vector_max)) <= 1e-6