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Decode the Coding Interview in Python: Real-World Examples

Sparse Matrix Multiplication

Explore methods to multiply sparse matrices by converting them into HashMaps to store non-zero elements only. Understand how to efficiently perform matrix multiplication with improved time and space complexity. This lesson helps you apply sparse matrix techniques in coding interviews.

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Description

We have two sparse matrices, A and B.

“A sparse matrix is one in which most of the elements are zero.”

You need to multiply the two matrices and return the output matrix. You can assume that A’s column number is equal to B’s row number.

Constraints

The following are some constraints:

  • 1 <= A.length, B.length <= 100
  • 1 <= A[i].length, B[i].length <= 100
  • -100 <= A[i][j], B[i][j] <= 100

Let’s review this scenario using the example below:

Coding exercise

Python 3.5
def multiply(A, B):
    pass
Sparse Matrix Multiplication

Solution

We have two sparse matrices, meaning most of the matrices’ elements are zero. We can represent the input matrices as hashmaps, and only save the non-zero elements and ...