Implement Strassen's matrix multiplication algorithm

This file implements Strassen's matrix multiplication algorithm, which is faster than the standard O(n^3) method for large matrices. It includes helper functions for matrix operations and benchmarks the performance of Strassen's algorithm against standard multiplication.

Author: ITZ-NIHALPATEL

matrix/strassen.py

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+"""
+Implementation of Strassen's matrix multiplication algorithm.
+https://en.wikipedia.org/wiki/Strassen_algorithm
+
+This is a divide-and-conquer algorithm that is asymptotically faster
+than the standard O(n^3) matrix multiplication for large matrices.
+
+Note: In Python, due to the overhead of recursion and list slicing,
+this implementation will be *slower* than the iterative version
+for small or medium-sized matrices (like 4x4).
+"""
+
+# type Matrix = list[list[int]]  # psf/black currently fails on this line
+Matrix = list[list[int]]
+
+# --- Test Matrices (reused from other files) ---
+matrix_1_to_4 = [
+    [1, 2],
+    [3, 4],
+]
+
+matrix_5_to_8 = [
+    [5, 6],
+    [7, 8],
+]
+
+matrix_count_up = [
+    [1, 2, 3, 4],
+    [5, 6, 7, 8],
+    [9, 10, 11, 12],
+    [13, 14, 15, 16],
+]
+
+matrix_unordered = [
+    [5, 8, 1, 2],
+    [6, 7, 3, 0],
+    [4, 5, 9, 1],
+    [2, 6, 10, 14],
+]
+
+matrix_non_square = [
+    [1, 2, 3],
+    [4, 5, 6],
+]
+
+
+# --- Helper function from matrix_multiplication_recursion.py ---
+def is_square(matrix: Matrix) -> bool:
+    """
+    Checks if a matrix is square.
+    >>> is_square(matrix_1_to_4)
+    True
+    >>> is_square(matrix_non_square)
+    False
+    """
+    len_matrix = len(matrix)
+    return all(len(row) == len_matrix for row in matrix)
+
+
+# --- Helper function for benchmarking ---
+def matrix_multiply(matrix_a: Matrix, matrix_b: Matrix) -> Matrix:
+    """
+    Standard iterative matrix multiplication for comparison.
+    >>> matrix_multiply(matrix_1_to_4, matrix_5_to_8)
+    [[19, 22], [43, 50]]
+    """
+    return [
+        [sum(a * b for a, b in zip(row, col)) for col in zip(*matrix_b)]
+        for row in matrix_a
+    ]
+
+
+# --- Helper functions for Strassen's Algorithm ---
+
+
+def matrix_add(matrix_a: Matrix, matrix_b: Matrix) -> Matrix:
+    """
+    Adds two matrices element-wise.
+    >>> matrix_add(matrix_1_to_4, matrix_5_to_8)
+    [[6, 8], [10, 12]]
+    """
+    return [
+        [matrix_a[i][j] + matrix_b[i][j] for j in range(len(matrix_a[0]))]
+        for i in range(len(matrix_a))
+    ]
+
+
+def matrix_subtract(matrix_a: Matrix, matrix_b: Matrix) -> Matrix:
+    """
+    Subtracts matrix_b from matrix_a element-wise.
+    >>> matrix_subtract(matrix_5_to_8, matrix_1_to_4)
+    [[4, 4], [4, 4]]
+    """
+    return [
+        [matrix_a[i][j] - matrix_b[i][j] for j in range(len(matrix_a[0]))]
+        for i in range(len(matrix_a))
+    ]
+
+
+def split_matrix(matrix: Matrix) -> tuple[Matrix, Matrix, Matrix, Matrix]:
+    """
+    Splits a given matrix into four equal quadrants.
+    >>> a, b, c, d = split_matrix(matrix_count_up)
+    >>> a
+    [[1, 2], [5, 6]]
+    >>> b
+    [[3, 4], [7, 8]]
+    >>> c
+    [[9, 10], [13, 14]]
+    >>> d
+    [[11, 12], [15, 16]]
+    """
+    n = len(matrix) // 2
+    a11 = [row[:n] for row in matrix[:n]]
+    a12 = [row[n:] for row in matrix[:n]]
+    a21 = [row[:n] for row in matrix[n:]]
+    a22 = [row[n:] for row in matrix[n:]]
+    return a11, a12, a21, a22
+
+
+def combine_matrices(
+    c11: Matrix, c12: Matrix, c21: Matrix, c22: Matrix
+) -> Matrix:
+    """
+    Combines four quadrants into a single matrix.
+    >>> a, b, c, d = split_matrix(matrix_count_up)
+    >>> combine_matrices(a, b, c, d) == matrix_count_up
+    True
+    """
+    n = len(c11)
+    result = []
+    for i in range(n):
+        result.append(c11[i] + c12[i])
+    for i in range(n):
+        result.append(c21[i] + c22[i])
+    return result
+
+
+def pad_matrix(matrix: Matrix, target_size: int) -> Matrix:
+    """Pads a matrix with zeros to reach the target_size."""
+    n = len(matrix)
+    if n == target_size:
+        return matrix
+
+    padded_matrix = [[0] * target_size for _ in range(target_size)]
+    for i in range(n):
+        for j in range(len(matrix[i])):
+            padded_matrix[i][j] = matrix[i][j]
+    return padded_matrix
+
+
+def unpad_matrix(matrix: Matrix, original_size: int) -> Matrix:
+    """Removes padding to return to the original_size."""
+    if len(matrix) == original_size:
+        return matrix
+    return [row[:original_size] for row in matrix[:original_size]]
+
+
+# --- Main Strassen Function ---
+
+
+def strassen(matrix_a: Matrix, matrix_b: Matrix) -> Matrix:
+    """
+    :param matrix_a: A square Matrix.
+    :param matrix_b: Another square Matrix with the same dimensions as matrix_a.
+    :return: Result of matrix_a * matrix_b.
+    :raises ValueError: If the matrices cannot be multiplied.
+
+    >>> strassen([], [])
+    []
+    >>> strassen(matrix_1_to_4, matrix_5_to_8)
+    [[19, 22], [43, 50]]
+    >>> strassen(matrix_count_up, matrix_unordered)
+    [[37, 61, 74, 61], [105, 165, 166, 129], [173, 269, 258, 197], [241, 373, 350, 265]]
+    >>> strassen(matrix_1_to_4, matrix_non_square)
+    Traceback (most recent call last):
+        ...
+    ValueError: Matrices must be square and of the same dimensions
+    >>> strassen(matrix_1_to_4, matrix_count_up)
+    Traceback (most recent call last):
+        ...
+    ValueError: Matrices must be square and of the same dimensions
+    """
+    if not matrix_a or not matrix_b:
+        return []
+
+    if not (
+        len(matrix_a) == len(matrix_b)
+        and is_square(matrix_a)
+        and is_square(matrix_b)
+    ):
+        raise ValueError("Matrices must be square and of the same dimensions")
+
+    original_size = len(matrix_a)
+
+    # Base case
+    if original_size == 1:
+        return [[matrix_a[0][0] * matrix_b[0][0]]]
+
+    # Pad matrix to the next power of 2
+    n = original_size
+    if n & (n - 1) != 0:
+        next_power_of_2 = 1 << n.bit_length()
+        a = pad_matrix(matrix_a, next_power_of_2)
+        b = pad_matrix(matrix_b, next_power_of_2)
+        n = next_power_of_2
+    else:
+        a = matrix_a
+        b = matrix_b
+
+    # Split matrices into quadrants
+    a11, a12, a21, a22 = split_matrix(a)
+    b11, b12, b21, b22 = split_matrix(b)
+
+    # Calculate the 7 Strassen products recursively
+    p1 = strassen(a11, matrix_subtract(b12, b22))
+    p2 = strassen(matrix_add(a11, a12), b22)
+    p3 = strassen(matrix_add(a21, a22), b11)
+    p4 = strassen(a22, matrix_subtract(b21, b11))
+    p5 = strassen(matrix_add(a11, a22), matrix_add(b11, b22))
+    p6 = strassen(matrix_subtract(a12, a22), matrix_add(b21, b22))
+    p7 = strassen(matrix_subtract(a11, a21), matrix_add(b11, b12))
+
+    # Calculate result quadrants
+    c11 = matrix_add(matrix_subtract(matrix_add(p5, p4), p2), p6)
+    c12 = matrix_add(p1, p2)
+    c21 = matrix_add(p3, p4)
+    c22 = matrix_subtract(matrix_subtract(matrix_add(p5, p1), p3), p7)
+
+    # Combine result quadrants
+    result = combine_matrices(c11, c12, c21, c22)
+
+    # Unpad the result to match original dimensions
+    return unpad_matrix(result, original_size)
+
+
+if __name__ == "__main__":
+    from doctest import testmod
+
+    failure_count, test_count = testmod()
+    if not failure_count:
+        print("\nBenchmark (Note: Strassen has high overhead in Python):")
+        from functools import partial
+        from timeit import timeit
+
+        # Run fewer iterations as Strassen is slower for small matrices in Python
+        mytimeit = partial(timeit, globals=globals(), number=10_0DENIED)
+        for func in ("matrix_multiply", "strassen"):
+            print(
+                f"{func:>25}(): "
+                f"{mytimeit(f'{func}(matrix_count_up, matrix_unordered)')}"
+            )