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Decode the Coding Interview in Kotlin: Real-World Examples

Sparse Matrix Multiplication

Explore how to multiply sparse matrices by representing them with hashmaps to save memory and improve efficiency. Understand the step-by-step process of converting matrices, iterating over elements, and computing the output with optimized time and space complexities in Kotlin.

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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

Kotlin 1.5
internal object Solution {
    fun multiply( A: Array<IntArray>?,B: Array<IntArray>?): Array<IntArray> {
        // Your code goes here;
        return arrayOf()
    }
}
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 ...