CSR矩阵 - 矩阵乘法

Question

I have two square matrices A and B

I must convert B to CSR Format and determine the product C

A * B_csr = C

I have found a lot of information online regarding CSR Matrix - Vector multiplication . The algorithm is:

for (k = 0; k < N; k = k + 1)
  result[i] = 0;

for (i = 0; i < N; i = i + 1)
{  
  for (k = RowPtr[i]; k < RowPtr[i+1]; k = k + 1)
  {  
    result[i] = result[i] + Val[k]*d[Col[k]];
  }  
}

However, I require Matrix - Matrix multiplication.

Further, it seems that most algorithms apply A_csr - vector multiplication where I require A * B_csr . My solution is to transpose the two matrices before converting then transpose the final product.

Can someone explain how to compute a Matrix - CSR Matrix product and/or a CSR Matrix - Matrix product?

Answer 1

Here is a simple solution in Python for the Dense Matrix X CSR Matrix . It should be self-explanatory.

def main():
  # 4 x 4 csr matrix
  #    [1, 0, 0, 0],
  #    [2, 0, 3, 0],
  #    [0, 0, 0, 0],
  #    [0, 4, 0, 0],
  csr_values = [1, 2, 3, 4]
  col_idx    = [0, 0, 2, 1]
  row_ptr    = [0, 1, 3, 3, 4]
  csr_matrix = [
      csr_values,
      col_idx,
      row_ptr
      ]

  dense_matrix = [
      [1, 3, 3, 4],
      [1, 2, 3, 4],
      [1, 4, 3, 4],
      [1, 2, 3, 5],
      ]

  res = [
      [0, 0, 0, 0],
      [0, 0, 0, 0],
      [0, 0, 0, 0],
      [0, 0, 0, 0],
      ]

  # matrix order, assumes both matrices are square
  n = len(dense_matrix)

  # res = dense X csr
  csr_row = 0 # Current row in CSR matrix
  for i in range(n):
    start, end = row_ptr[i], row_ptr[i + 1]
    for j in range(start, end):
      col, csr_value = col_idx[j], csr_values[j]
      for k in range(n):
        dense_value = dense_matrix[k][csr_row]
        res[k][col] += csr_value * dense_value
    csr_row += 1

  print res


if __name__ == '__main__':
  main()

CSR Matrix X Dense Matrix is really just a sequence of CSR Matrix X Vector product for each row of the dense matrix right? So it should be really easy to extend the code you show above to do this.

Moving forward, I suggest you don't code these routines yourself. If you are using C++ (based on the tag), then you could have a look at Boost ublas for example, or Eigen . The APIs may seem a bit cryptic at first but it's really worth it in the long term. First, you gain access to a lot more functionality, which you will probably require in the future. Second these implementations will be better optimised.