From dc11ca7ba285f908db5d68a5a55eb901ab6522be Mon Sep 17 00:00:00 2001
From: saahil-mahato <saahilmahato11@gmail.com>
Date: Thu, 5 Oct 2023 21:55:57 +0545
Subject: [PATCH 1/2] refactor: fix strassen matrix multiplication docs
---
divide_and_conquer/strassen_matrix_multiplication.py | 2 +-
1 file changed, 1 insertion(+), 1 deletion(-)
diff --git a/divide_and_conquer/strassen_matrix_multiplication.py b/divide_and_conquer/strassen_matrix_multiplication.py
index 1d03950ef9fe..275c64b3df94 100644
--- a/divide_and_conquer/strassen_matrix_multiplication.py
+++ b/divide_and_conquer/strassen_matrix_multiplication.py
@@ -74,7 +74,7 @@ def print_matrix(matrix: list) -> None:
def actual_strassen(matrix_a: list, matrix_b: list) -> list:
"""
Recursive function to calculate the product of two matrices, using the Strassen
- Algorithm. It only supports even length matrices.
+ Algorithm. It only supports square matrices of any size that is a power of 2.
"""
if matrix_dimensions(matrix_a) == (2, 2):
return default_matrix_multiplication(matrix_a, matrix_b)
From bd2cee97558f280f131b367ec684bee1af2ea764 Mon Sep 17 00:00:00 2001
From: saahil-mahato <saahilmahato11@gmail.com>
Date: Thu, 5 Oct 2023 22:05:09 +0545
Subject: [PATCH 2/2] refactor: make docs more clear
---
divide_and_conquer/strassen_matrix_multiplication.py | 4 ++--
1 file changed, 2 insertions(+), 2 deletions(-)
diff --git a/divide_and_conquer/strassen_matrix_multiplication.py b/divide_and_conquer/strassen_matrix_multiplication.py
index 275c64b3df94..f529a255d2ef 100644
--- a/divide_and_conquer/strassen_matrix_multiplication.py
+++ b/divide_and_conquer/strassen_matrix_multiplication.py
@@ -129,8 +129,8 @@ def strassen(matrix1: list, matrix2: list) -> list:
new_matrix1 = matrix1
new_matrix2 = matrix2
- # Adding zeros to the matrices so that the arrays dimensions are the same and also
- # power of 2
+ # Adding zeros to the matrices to convert them both into square matrices of equal
+ # dimensions that are a power of 2
for i in range(maxim):
if i < dimension1[0]:
for _ in range(dimension1[1], maxim):