CSR矩阵 - 矩阵乘法 [英] CSR Matrix - Matrix multiplication
问题描述
我有两个方形矩阵 A
和 B
我必须将 B
转换为 CSR格式
,并确定产品 C
A * B_csr = C
我在网上找到很多有关 CSR矩阵 - 向量乘法。算法是:
for(k = 0; k
result [i] = 0;
for(i = 0; i{
for(k = RowPtr [i]; k{
result [i] = result [i] + Val [k] * d [Col [k]
}
}
但是,我需要
Matrix - Matrix
乘法。
此外,似乎大多数算法适用
A_csr - vector
乘法,其中我需要A * B_csr
。我的解决方案是转换两个矩阵,然后转换,然后转置最终产品。
有人可以解释如何计算
Matrix - CSR Matrix
产品和/或
CSR Matrix - Matrix
产品?解决方案这里是一个简单的Python解决方案,用于
Dense Matrix X CSR Matrix
。它应该是不言自明的。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],
] b
res = [
[0,0,0,0],
[0,0,0,0],
[0,0,0,0]
[0,0,0,0],
]
#矩阵顺序,假设两个矩阵都是平方的
n = len(dense_matrix)
$范围(n)中的i的
开始,结束= row_ptr [i],row_ptr [i],b_br + 1]
for范围(开始,结束):
col,csr_value = col_idx [j],csr_values [j]
用于范围dense_value = dense_matrix [k] [csr_row]
res [k] [col] + = csr_value * dense_value
csr_row + = 1
print res
$ b b
if __name__ =='__main__':
main()
code> CSR Matrix X Dense Matrix 实际上只是一系列
CSR Matrix X Vector
产品的每一行密集矩阵?因此,应该很容易扩展上面显示的代码来做到这一点。
前进,我建议你不要自己编写这些例程。如果您使用C ++(基于标记),那么您可以查看 Boost ublas ,或 Eigen 。 API可能看起来有点神秘,但它是非常值得的长期。首先,您可以访问更多的功能,这可能需要在将来。第二,这些实现将被更好地优化。
I have two square matrices
A
andB
I must convert
B
toCSR Format
and determine the productC
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 requireA * 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 aCSR Matrix - Matrix
product?解决方案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 ofCSR 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.
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