Consider the following problem from Jeff Erickson: Algorithms that also appears in this post, which wants us to prove a lower bound for the problem.

Suppose you are given an array $A[1 .. n]$ of integers, each of which may be positive, negative, or zero. A contiguous subarray $A[i .. j]$ is called a positive interval if the sum of its entries is greater than zero. Describe and analyze an algorithm to compute the minimum number of positive intervals that cover every positive entry in $A$. For example, given the following array as input, your algorithm should output $3$. If every entry in the input array is negative, your algorithm should output $0$.
$${+}3\ {-}5\ {+}7\ {-}4\ {+}1\ {-}8\ {+}3\ {-}7\ {+}5\ {-}9\ {+}5\ {-}2\ {+}4$$

My question is, is there a greedy approach for this problem?

I have an idea, find the largest positive number, then according to whether there is some positive numbers in each side, go left or right to cover more positive number. But I think this idea doesn't work.

  • $\begingroup$ It looks like your idea is a bit too complicated and unlikely to work. $\endgroup$
    – John L.
    Commented Apr 30, 2022 at 0:42
  • $\begingroup$ We ask that references satisfy scholarly requirements: please include at least the title of the book and the full name of the author, and the chapter and exercise number, so that others with the same question are more likely to be able to find this page by search. Thank you! $\endgroup$
    – D.W.
    Commented Apr 30, 2022 at 20:05
  • $\begingroup$ Please clarify in the question: are intervals allowed to overlap? $\endgroup$
    – greybeard
    Commented May 18, 2023 at 7:55

3 Answers 3


There is a simple greedy algorithm.

A greedy approach

The greedy idea is obvious: starting from the very left, extend the coverage to the right as far as possible each time.

However, it may not be obvious to get it right. We should search each next positive interval by the following two criteria:

  1. It should cover the next uncovered positive entry.
  2. It should cover as many entries as possible after that entry among all positive intervals that covers that entry.

To boost performance, after the next $u$ncovered positive entry $A[u]$ is found, we will find the optimal start of the positive interval to cover $A[u]$ and the most entries after $A[u]$. That optimal start is an entry such that the sum of entries between it and $A[u]$ is the largest. In fact, that optimal start can be ignored; knowing that largest sum is enough.

Let $\text{sum}(A[i..j])=A[i]+A[i+1]+\cdots+A[j]$, the sum of entries in $A[i..j]$.

Here is the greedy algorithm.

$u\leftarrow0.$ $\ rightmost\leftarrow0.$ $\ answer\leftarrow 0.$ Repeat the following $3$ steps.

  1. $u\leftarrow$ the least index greater than $rightmost$ at which the entry of $A$ is positive.
    If there is no such index, return $answer$.
    Otherwise, $answer\leftarrow answer+1$. We will cover $A[u]>0$ and as many entries as possible after it with one positive interval.
  2. $surplus\leftarrow$ the largest sum among $\text{sum}(A[u..u])$, $\text{sum}(A[u-1..u])$, $\cdots$, $\text{sum}(A[1..u])$.
    For the sake of correctness proof, let $start$ be the corresponding starting index of $surplus$, which is the optimal start of the desired positive interval.
  3. $rightmost\leftarrow$ the largest index $k$ such that $ surplus+\text{sum}(A[u+1..k])>0$, where $u+1\le k\le n$. If there is no such index, $rightmost\leftarrow u$.
    Conceptually, we select the positive interval $A[start, rightmost]$.

Complexity Analysis

Each one of step 1, 2 and 3 takes $O(n)$ time.

$u$ starts at $0$ and ends at most $n$. Since $u\le n$ always increases at step 1 unless the algorithm returns, step 1 and hence the whole loop body of step 1, 2, 3 can repeat at most $n$ times. Hence the whole algorithm takes $O(n^2)$ time.

The post here optimizes the approach above to run in $O(n)$ time.

Correctness proof

Let $A[s_1..r_1], A[s_2..r_2], \cdots$ be the positive intervals selected successively by the greedy algorithm. So $r_1<r_2<\cdots$.
Let $A[s_1'..r_1'], A[s_2'..r_2'], \cdots$ be some positive intervals that covers all positive entries in $A$. WLOG, assume $r_1'<r_2'<\cdots$.

Let $s_0=r_0=s_0'=r_0'=0$.

It is enough to prove the greedy algorithm "stays ahead", i.e., $r_i\ge r_i'$ for all $i$.

The case $i=0$ is trivial. Suppose $r_i\ge r_i'$ for some $i$.

Let $u$ and $start=s_{i+1}$ be the values found in the step 1 and 2 of the algorithm that leads to the selected interval $A[s_{i+1}..r_{i+1}]$, i.e., $A[u]$ is the positive entry after $A[r_i]$ and $\text{sum}(A[start..u])$ is the largest among $\text{sum}(A[*..u])$.

Since $r_i'\le r_i<u$, $A[u]$ is not covered by any interval $A[s_j'.. r_j']$, $1\le j\le i$. Hence $A[u]$ is in some $A[s_k'..r_k']$, where $k\ge i+1$. $$ \text{sum}(A[u+1..r_k'])=\text{sum}(A[s_k'..r_k'])-\text{sum}(A[s_k'..u])>0-\text{sum}(A[start..u])$$ By the step $3$ of the algorithm, the inequality above implies $r_{i+1}\ge r_k'$. Hence $r_{i+1}\ge r_k'\ge r_{i+1}'$.

A Python implementation

While the indices above start with $1$, they start with $0$ in the implementation.

from itertools import accumulate

def minimum_cover(a):
    n = len(a)
    answer = 0
    rightmost = -1
    while True:
        uncovered = rightmost + 1
        while uncovered < n and a[uncovered] <= 0:
            uncovered += 1
        if uncovered == n:
        surplus = max(accumulate(a[uncovered::-1]))
        new_rightmost = uncovered + 1 + max(
            (idx for idx, sm in enumerate(accumulate(a[uncovered + 1:n]))
             if surplus + sm > 0), default=-1)
        if new_rightmost > rightmost:
            rightmost = new_rightmost
            answer += 1
    return answer

print(minimum_cover([1, 7, -9, 2, -10, -5, 5, -10, 4, -2, 3, -1, 2]))  # 2
print(minimum_cover([1, -3, 3, -2, 1, -2, 1]))  # 2
  • $\begingroup$ Overlapping positive intervals are needed to cover [1, -2, 1, -2, 3, -2, 1, -2, 1] with minimum number of positive intervals. Ditto for [1, -7, 3, -5, 9, -6, 4, -8, 2]. $\endgroup$
    – John L.
    Commented May 17, 2023 at 13:54

The accepted greedy solution doesn't work.

I don't think there is a greedy algorithm. There is a counterexample to the other solution, where they use the heuristic of adding the largest positive interval starting at the current positive integer.

Consider the counterexample:

[1, -10, 10, -8, 3, -6, 4, -10, 8]

The proposed greedy algorithm will cover the array with 3 intervals:

(1, -10, 10) (3, -6, 4) (8)

But the array can be optimally covered with just 2 intervals:

(1) (10, -8, 3, -6, 4, -10, 8)
  • $\begingroup$ Thanks for catching my error. Please check my updated answer. $\endgroup$
    – John L.
    Commented Dec 9, 2022 at 14:01

When we have an array like this: [1, -6, 5, -100, 101, -8, 3, -6, 4, -90, 8],

and apply the above greedy algorithm to it, the outcome is 2

However, I think that the correct answer should be 3.

  • $\begingroup$ It is not required that the positive intervals are disjoint. sum([1, -6, 5, -100, 101]$=1>0$.$\quad$ sum([101, -8, 3, -6, 4, -90, 8]$=12>0$. The correct outcome should be $2$. $\endgroup$
    – John L.
    Commented May 17, 2023 at 12:44
  • $\begingroup$ Please clarify in your answer: Does above greedy algorithm refer to ErroR's or John L's? $\endgroup$
    – greybeard
    Commented May 18, 2023 at 9:34
  • $\begingroup$ It refers to John L's. but I misunderstood the problem. I didn't know overlapping was possible. Thank you for pointing out the part I misunderstood. $\endgroup$
    – cv_oh
    Commented May 18, 2023 at 13:33

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