@All... Infact if we closely observe, then for A[row][col] when replaced with A[row+1][0].. Then on heapifying the matrix rooted at A[row+1][0], the new value will have shifts within the submatrix (A[row+1][0] , A[M] [col])
Hence, the actual time complexity would be : Say, no of rows = M and no. of cols = N, Time complexity = sum over all i (1 to M} { sum over all j (1 to N} { (M - i + j) } } = M*N*(M + N)/2 On Jan 11, 2:27 pm, Lucifer <sourabhd2...@gmail.com> wrote: > @dipit .. > > Yup you are correct.. > > Say, no of rows = M and no. of cols = N, > Time complexity = sum over all i (1 to M} { N*(M+N-i) } > = M * N * (M + 2N - 1) /2 > > On Jan 11, 2:19 pm, Dipit Grover <dipitgro...@gmail.com> wrote: > > > > > > > > > @Lucifer : I came up with a similar algorithm as yours but I dont > > understand your complexity analysis : " sum over all i (1 to M} { > > i*(M+N-i)} " . > > > Shouldnt it be " M * sum over all i(1 to N) {(M+N-i)} " ? M= no of > > columns, N= no of rows . Since we always have the min element at the 0th > > column of the next row for each element of the current row. -- You received this message because you are subscribed to the Google Groups "Algorithm Geeks" group. To post to this group, send email to algogeeks@googlegroups.com. To unsubscribe from this group, send email to algogeeks+unsubscr...@googlegroups.com. For more options, visit this group at http://groups.google.com/group/algogeeks?hl=en.