This exercise involves manipulation of sparse matrices. Given two sparse matrices A (m x n) & B (n x p), you need to transpose both and then multiply BT (p x n) with AT (n x m). BT is the transpose of B and AT is the transpose of A. The matrices will be specified using the doubly compressed sparse column (DCSC) format (Ref: Slides 84, 85 in the Sparse Matrices PPT). Ideally, you should manipulate the matrices using the DCSC format itself. However, you are welcome to convert them to any other format (CSC / Triples / etc.) if you prefer. You may also use libraries such as numpy and ndarray. After transposing the two matrices, you need to compute their product (BT . AT) as a sum of non-zero rank-1 matrices, i.e., sum of outer products of non-zero columns in BT with their corresponding non-zero rows in AT.
