Given two sparse matrices A and B, return the result of AB.
You may assume that A's column number is equal to B's row number.
Example:
A = [
[ 1, 0, 0],
[-1, 0, 3]
]
B = [
[ 7, 0, 0 ],
[ 0, 0, 0 ],
[ 0, 0, 1 ]
]
| 1 0 0 | | 7 0 0 | | 7 0 0 |
AB = | -1 0 3 | x | 0 0 0 | = | -7 0 3 |
| 0 0 1 |
Solution1:利用线性叠加的 Regular 方法
思路: 利用矩阵相乘 的 行的线性叠加,可参考://www.greatytc.com/p/11d373b4f6be 中第三个
Time Complexity: O(n * m * nB) Space Complexity: O(1) (avg取决于sparse程度)
行列数:mn * nnB
Solution2:线性叠加形式 + Sparse 方式
思路:计算思路和Solution1相同,但是是先建好sparse的表示,
A sparse matrix can be represented as a sequence of rows, each of which is a sequence of (column-number, value) pairs of the nonzero values in the row.
再一起计算。
(其实一样的,因为solution1中是0也break了;Solution2这样写就是分开步骤了)
参考:http://www.cs.cmu.edu/~scandal/cacm/node9.html
Time Complexity: O(n * m * nB) Space Complexity: O(m *n) (avg取决于sparse程度)
Solution1 Code:
// A: m * n, B: n * nB, C: m * nB
public class Solution {
public int[][] multiply(int[][] A, int[][] B) {
int m = A.length,n = A[0].length, nB = B[0].length;
int[][] C = new int[m][nB];
for(int i = 0; i < m; i++) {
for(int k = 0; k < n; k++) {
if (A[i][k] != 0) {
for (int j = 0; j < nB; j++) {
if (B[k][j] != 0) C[i][j] += A[i][k] * B[k][j];
}
}
}
}
return C;
}
}
Solution2 Code:
public class Solution {
public int[][] multiply(int[][] A, int[][] B) {
int m = A.length, n = A[0].length, nB = B[0].length;
int[][] result = new int[m][nB];
// representation indexA build
List[] indexA = new List[m];
for(int i = 0; i < m; i++) {
List<Integer> numsA = new ArrayList<>();
for(int k = 0; k < n; k++) {
if(A[i][k] != 0){
numsA.add(k);
numsA.add(A[i][k]);
}
}
indexA[i] = numsA;
}
// linear combination of rows
for(int i = 0; i < m; i++) {
List<Integer> numsA = indexA[i];
for(int p = 0; p < numsA.size() - 1; p += 2) {
int colA = numsA.get(p);
int valA = numsA.get(p + 1);
for(int j = 0; j < nB; j ++) {
int valB = B[colA][j];
result[i][j] += valA * valB;
}
}
}
return result;
}
}