Sparse Matrix Implementation | Dataford Interview Questions

Implement a sparse matrix class, including efficient addition and multiplication methods.

Represent each matrix as dimensions plus nonzero coordinate entries. Implement to_dict(), add(other), and multiply(other), removing zero-valued results and returning entries sorted by row and column. The grading function constructs two matrices and returns both operation results.

Contract

Input matrices are dictionaries with rows, cols, and entries, where each entry is [row, col, value]. Return {"sum": object or null, "product": object or null}. Addition requires equal dimensions; multiplication requires matrix_a.cols == matrix_b.rows.

Constraints

Rows and columns are positive integers
Each entry has the form [row, col, value]
Entry coordinates are valid for their matrix
Values are integers
Duplicate coordinates may occur
Addition requires equal dimensions
Multiplication requires matrix_a.cols == matrix_b.rows

Function Signature

def sparse_matrix_operations(matrix_a, matrix_b):

Your question is Sparse Matrix Implementation. Start with the requirements in the Question tab.

Run and submit as often as you like. When you're ready, talk me through your approach or go straight to the code.

Problem