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A matrix $A\in \mathbb{R}^{m\times n}$ is given with the property

$$a_{i,j}\leq a_{i,j + 1}\quad\text{and}\quad a_{i, j}\leq a_{i + 1, j}\text{,}$$ e.g.

$$ A = \begin{bmatrix} -4 & -2 & -1 & 7\\ -3 & 2 & 3 & 7 \\ -1 & 4 & 9 & 10 \end{bmatrix}\text{.} $$

A submatrix of matrix $A$ which contains rows $i$, $i_0 \leq i \leq i_1$, and columns $j$, $j_0\leq j\leq j_1$, is denoted by $A[i_0:i_1, j_0:j_1]$. Without any loss, we can assume that $m\leq n$, otherwise, we change the roles of rows and columns.

I have four solutions for an algorithm that finds the number of negative elements $a_{i,j}$ in $A$:

  1. A brute force one that checks every element of $A$: $\mathcal{O}(nm)$
  2. An improved version of 1, where we start traversal in the $i$-th row at the index of the last negative element in the previous row (in the first row, we start at $n$): $\mathcal{O}(m + n)$.
  3. An improved version of 2., where binary search is used for finding the last negative number in a given row.
  4. Recursive algorithm $F$, which is an improved version of 3:
    • find the index $J$ of the last negative number in the row $j=m/2$
    • let $t_1 = F(A[1:j - 1, J + 1:n])$ and $t_2 = F(A[j + 1:m,1:J])$
    • return $Jj + t_1 + t_2$

So ... The third algorithm is in $\mathcal{O}(m\log n)$, since it can happen that each row takes $\mathcal{O}(\log n)$ steps (if all elements of $A$ are negative).

It seems that the fourth algorithm is considerably faster, since the aforementioned worst case would take only $\mathcal{O}(\log m\log n)$ steps, since all $t_1$s would be computed in $\mathcal{O}(1)$, but I am not able to prove that.

What is the exact time complexity $T(m, n)$ of the fourth algorithm?

This is the solution of the recursion

$$T(m, n) = \mathcal{O}(\log n) + \max_J T(m/2, n - J) + T(m/2, J)$$

If I try to plug in $T(m, n)\leq c\log n\log m$ into RHS, I found out that

$$\text{RHS}\leq c_1\log n + c\max_J (\log (m/2) \log J + (\log m/2)\log (n -J))\text{,}$$ so $\max$ is achieved at $J = n/2$, but from here, I cannot proceed further than

$$\text{RHS}\leq c_1\log n + c(\log m - 1)(2\log n - 2)$$ or simplifying that if we note that $c_1 < 2$ and start with the assumption that $c \geq 2$.

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Algorithm 2 is linear with $O(n+m)$, and there is no hope of finding a significant improvement over that in the case where $n$ and $m$ are approximately equally large. Namely, consider the matrix $$ \begin{bmatrix} -1 & -1 & -1 & -1 & ? \\ -1 & -1 & -1 & ? & 1 \\ -1 & -1 & ? & 1 & 1 \\ -1 & ? & 1 & 1 & 1 \\ ? & 1 & 1 & 1 & 1 \end{bmatrix} $$ Even if you can read all of these entries for free you will still need to inspect each of the antidiagonal elements in order to find the number of negatives.


Algorithm 3 is not actually an improvement. Even though a binary search has a better worst-case behavior on a given row, the linear search is faster in the particular case that the positive/negative boundary is close to the one from the previous row -- and that is particular the case where we need to conserve energy because the problem in the remaining rows is more complex.

You could use a hybrid approach in algorithm 3, where in each row you start by moving left by 1 step, then by 2 steps, then 4, then 8 ... and once you find a difference use binary search between the two last tries. This would allow us to get down to something like $O(\max(n,m \log n))$.


From the above we can see that $O(\log n\log m)$ cannot possibly be correct. No matter what the constant factors are, $O(\log n\log m)$ would eventually be smaller than the number of rows.

But even when $m\ll n$, it is easy to construct examples where you need to inspect at least one value in each row before you can know how many negatives there are. So you can't possibly get below $O(m)$ running time.

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Shorn of the preamble, you ask for the asymptotic growth rate of a function $T(m,n)$ satisfying $$ T(m,n)=O(\log m)+\max_{1\leq J\leq n}\bigl[T(m/2,n-J)+T(m/2,J)\bigr]. $$ And in fact, you believe it is $O(\log m\log n)$. Unfortunately, this belief is false, since the function $T(m,n)=n$ satisfies the recurrence $$ T(m,n)=\max_{1\leq J\leq n}\bigl[T(m/2,n-J)+T(m/2,J)\bigr]. $$

This means you will need to work a little harder to show that your algorithm is fast, since the recurrence you wrote down is not enough to give a mathematical proof that it is fast.

Note. The "problem" is that each time you cut $m$ in half, you have two subproblems of comparable difficulty of size $n/2$. This won't give you logarithmic complexity, for that you would need there to be only one such subproblem.

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  • $\begingroup$ This means you will need to work a little harder to show that your algorithm is fast I think that this means that the algorithm is not that fast as I thought: you hint how to show that it is at least linear in one of the terms. $\endgroup$ – Antoine Apr 29 '17 at 21:32

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