LeetCode #2244 — MEDIUM

Minimum Rounds to Complete All Tasks

Move from brute-force thinking to an efficient approach using array strategy.

The Problem

Problem Statement

You are given a 0-indexed integer array tasks, where tasks[i] represents the difficulty level of a task. In each round, you can complete either 2 or 3 tasks of the same difficulty level.

Return the minimum rounds required to complete all the tasks, or -1 if it is not possible to complete all the tasks.

Example 1:

Input: tasks = [2,2,3,3,2,4,4,4,4,4]
Output: 4
Explanation: To complete all the tasks, a possible plan is:
- In the first round, you complete 3 tasks of difficulty level 2. 
- In the second round, you complete 2 tasks of difficulty level 3. 
- In the third round, you complete 3 tasks of difficulty level 4. 
- In the fourth round, you complete 2 tasks of difficulty level 4.  
It can be shown that all the tasks cannot be completed in fewer than 4 rounds, so the answer is 4.

Example 2:

Input: tasks = [2,3,3]
Output: -1
Explanation: There is only 1 task of difficulty level 2, but in each round, you can only complete either 2 or 3 tasks of the same difficulty level. Hence, you cannot complete all the tasks, and the answer is -1.

Constraints:

1 <= tasks.length <= 10⁵
1 <= tasks[i] <= 10⁹

Note: This question is the same as 2870: Minimum Number of Operations to Make Array Empty.

Step 01

Brute Force Baseline

Problem summary: You are given a 0-indexed integer array tasks, where tasks[i] represents the difficulty level of a task. In each round, you can complete either 2 or 3 tasks of the same difficulty level. Return the minimum rounds required to complete all the tasks, or -1 if it is not possible to complete all the tasks.

Baseline thinking

Start with the most direct exhaustive search. That gives a correctness anchor before optimizing.

Pattern signal: Array · Hash Map · Greedy

Example 1

[2,2,3,3,2,4,4,4,4,4]

Example 2

[2,3,3]

Core Insight

What unlocks the optimal approach

Which data structure can you use to store the number of tasks of each difficulty level?
For any particular difficulty level, what can be the optimal strategy to complete the tasks using minimum rounds?
When can we not complete all tasks of a difficulty level?

Interview move: turn each hint into an invariant you can check after every iteration/recursion step.

Step 03

Algorithm Walkthrough

Iteration Checklist

Define state (indices, window, stack, map, DP cell, or recursion frame).
Apply one transition step and update the invariant.
Record answer candidate when condition is met.
Continue until all input is consumed.

Use the first example testcase as your mental trace to verify each transition.

Step 04

Edge Cases

Minimum Input

Single element / shortest valid input

Validate boundary behavior before entering the main loop or recursion.

Duplicates & Repeats

Repeated values / repeated states

Decide whether duplicates should be merged, skipped, or counted explicitly.

Extreme Constraints

Upper-end input sizes

Re-check complexity target against constraints to avoid time-limit issues.

Invalid / Corner Shape

Empty collections, zeros, or disconnected structures

Handle special-case structure before the core algorithm path.

Step 05

Full Annotated Code

Source-backed implementations are provided below for direct study and interview prep.

// Accepted solution for LeetCode #2244: Minimum Rounds to Complete All Tasks
class Solution {
    public int minimumRounds(int[] tasks) {
        Map<Integer, Integer> cnt = new HashMap<>();
        for (int t : tasks) {
            cnt.merge(t, 1, Integer::sum);
        }
        int ans = 0;
        for (int v : cnt.values()) {
            if (v == 1) {
                return -1;
            }
            ans += v / 3 + (v % 3 == 0 ? 0 : 1);
        }
        return ans;
    }
}

// Accepted solution for LeetCode #2244: Minimum Rounds to Complete All Tasks
func minimumRounds(tasks []int) int {
	cnt := map[int]int{}
	for _, t := range tasks {
		cnt[t]++
	}
	ans := 0
	for _, v := range cnt {
		if v == 1 {
			return -1
		}
		ans += v / 3
		if v%3 != 0 {
			ans++
		}
	}
	return ans
}

# Accepted solution for LeetCode #2244: Minimum Rounds to Complete All Tasks
class Solution:
    def minimumRounds(self, tasks: List[int]) -> int:
        cnt = Counter(tasks)
        ans = 0
        for v in cnt.values():
            if v == 1:
                return -1
            ans += v // 3 + (v % 3 != 0)
        return ans

// Accepted solution for LeetCode #2244: Minimum Rounds to Complete All Tasks
use std::collections::HashMap;

impl Solution {
    pub fn minimum_rounds(tasks: Vec<i32>) -> i32 {
        let mut cnt = HashMap::new();
        for &t in tasks.iter() {
            let count = cnt.entry(t).or_insert(0);
            *count += 1;
        }
        let mut ans = 0;
        for &v in cnt.values() {
            if v == 1 {
                return -1;
            }
            ans += v / 3 + (if v % 3 == 0 { 0 } else { 1 });
        }
        ans
    }
}

// Accepted solution for LeetCode #2244: Minimum Rounds to Complete All Tasks
function minimumRounds(tasks: number[]): number {
    const cnt = new Map();
    for (const t of tasks) {
        cnt.set(t, (cnt.get(t) || 0) + 1);
    }
    let ans = 0;
    for (const v of cnt.values()) {
        if (v == 1) {
            return -1;
        }
        ans += Math.floor(v / 3) + (v % 3 === 0 ? 0 : 1);
    }
    return ans;
}

Step 06

Interactive Study Demo

Use this to step through a reusable interview workflow for this problem.

Custom checkpoints (one per line)

Press Step or Run All to begin.

Step 07

Complexity Analysis

Time

O(n)

Space

O(n)

Approach Breakdown

EXHAUSTIVE

O(2ⁿ) time

O(n) space

Try every possible combination of choices. With n items each having two states (include/exclude), the search space is 2ⁿ. Evaluating each combination takes O(n), giving O(n × 2ⁿ). The recursion stack or subset storage uses O(n) space.

GREEDY

O(n log n) time

O(1) space

Greedy algorithms typically sort the input (O(n log n)) then make a single pass (O(n)). The sort dominates. If the input is already sorted or the greedy choice can be computed without sorting, time drops to O(n). Proving greedy correctness (exchange argument) is harder than the implementation.

Shortcut: Sort + single pass → O(n log n). If no sort needed → O(n). The hard part is proving it works.

Coach Notes

Common Mistakes

Review these before coding to avoid predictable interview regressions.

Off-by-one on range boundaries

Wrong move: Loop endpoints miss first/last candidate.

Usually fails on: Fails on minimal arrays and exact-boundary answers.

Fix: Re-derive loops from inclusive/exclusive ranges before coding.

Mutating counts without cleanup

Wrong move: Zero-count keys stay in map and break distinct/count constraints.

Usually fails on: Window/map size checks are consistently off by one.

Fix: Delete keys when count reaches zero.

Using greedy without proof

Wrong move: Locally optimal choices may fail globally.

Usually fails on: Counterexamples appear on crafted input orderings.

Fix: Verify with exchange argument or monotonic objective before committing.