LeetCode #1899 — MEDIUM

Merge Triplets to Form Target Triplet

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

The Problem

Problem Statement

A triplet is an array of three integers. You are given a 2D integer array triplets, where triplets[i] = [a_i, b_i, c_i] describes the i^th triplet. You are also given an integer array target = [x, y, z] that describes the triplet you want to obtain.

To obtain target, you may apply the following operation on triplets any number of times (possibly zero):

Choose two indices (0-indexed) i and j (i != j) and update triplets[j] to become [max(a_i, a_j), max(b_i, b_j), max(c_i, c_j)].
- For example, if triplets[i] = [2, 5, 3] and triplets[j] = [1, 7, 5], triplets[j] will be updated to [max(2, 1), max(5, 7), max(3, 5)] = [2, 7, 5].

Return true if it is possible to obtain the target triplet [x, y, z] as an element of triplets, or false otherwise.

Example 1:

Input: triplets = [[2,5,3],[1,8,4],[1,7,5]], target = [2,7,5]
Output: true
Explanation: Perform the following operations:
- Choose the first and last triplets [[2,5,3],[1,8,4],[1,7,5]]. Update the last triplet to be [max(2,1), max(5,7), max(3,5)] = [2,7,5]. triplets = [[2,5,3],[1,8,4],[2,7,5]]
The target triplet [2,7,5] is now an element of triplets.

Example 2:

Input: triplets = [[3,4,5],[4,5,6]], target = [3,2,5]
Output: false
Explanation: It is impossible to have [3,2,5] as an element because there is no 2 in any of the triplets.

Example 3:

Input: triplets = [[2,5,3],[2,3,4],[1,2,5],[5,2,3]], target = [5,5,5]
Output: true
Explanation: Perform the following operations:
- Choose the first and third triplets [[2,5,3],[2,3,4],[1,2,5],[5,2,3]]. Update the third triplet to be [max(2,1), max(5,2), max(3,5)] = [2,5,5]. triplets = [[2,5,3],[2,3,4],[2,5,5],[5,2,3]].
- Choose the third and fourth triplets [[2,5,3],[2,3,4],[2,5,5],[5,2,3]]. Update the fourth triplet to be [max(2,5), max(5,2), max(5,3)] = [5,5,5]. triplets = [[2,5,3],[2,3,4],[2,5,5],[5,5,5]].
The target triplet [5,5,5] is now an element of triplets.

Constraints:

1 <= triplets.length <= 10⁵
triplets[i].length == target.length == 3
1 <= a_i, b_i, c_i, x, y, z <= 1000

Step 01

Brute Force Baseline

Problem summary: A triplet is an array of three integers. You are given a 2D integer array triplets, where triplets[i] = [ai, bi, ci] describes the ith triplet. You are also given an integer array target = [x, y, z] that describes the triplet you want to obtain. To obtain target, you may apply the following operation on triplets any number of times (possibly zero): Choose two indices (0-indexed) i and j (i != j) and update triplets[j] to become [max(ai, aj), max(bi, bj), max(ci, cj)]. For example, if triplets[i] = [2, 5, 3] and triplets[j] = [1, 7, 5], triplets[j] will be updated to [max(2, 1), max(5, 7), max(3, 5)] = [2, 7, 5]. Return true if it is possible to obtain the target triplet [x, y, z] as an element of triplets, or false otherwise.

Baseline thinking

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

Pattern signal: Array · Greedy

Example 1

[[2,5,3],[1,8,4],[1,7,5]]
[2,7,5]

Example 2

[[3,4,5],[4,5,6]]
[3,2,5]

Example 3

[[2,5,3],[2,3,4],[1,2,5],[5,2,3]]
[5,5,5]

Step 02

Core Insight

What unlocks the optimal approach

Which triplets do you actually care about?
What property of max can you use to solve the problem?

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 #1899: Merge Triplets to Form Target Triplet
class Solution {
    public boolean mergeTriplets(int[][] triplets, int[] target) {
        int x = target[0], y = target[1], z = target[2];
        int d = 0, e = 0, f = 0;
        for (var t : triplets) {
            int a = t[0], b = t[1], c = t[2];
            if (a <= x && b <= y && c <= z) {
                d = Math.max(d, a);
                e = Math.max(e, b);
                f = Math.max(f, c);
            }
        }
        return d == x && e == y && f == z;
    }
}

// Accepted solution for LeetCode #1899: Merge Triplets to Form Target Triplet
func mergeTriplets(triplets [][]int, target []int) bool {
	x, y, z := target[0], target[1], target[2]
	d, e, f := 0, 0, 0
	for _, t := range triplets {
		a, b, c := t[0], t[1], t[2]
		if a <= x && b <= y && c <= z {
			d = max(d, a)
			e = max(e, b)
			f = max(f, c)
		}
	}
	return d == x && e == y && f == z
}

# Accepted solution for LeetCode #1899: Merge Triplets to Form Target Triplet
class Solution:
    def mergeTriplets(self, triplets: List[List[int]], target: List[int]) -> bool:
        x, y, z = target
        d = e = f = 0
        for a, b, c in triplets:
            if a <= x and b <= y and c <= z:
                d = max(d, a)
                e = max(e, b)
                f = max(f, c)
        return [d, e, f] == target

// Accepted solution for LeetCode #1899: Merge Triplets to Form Target Triplet
impl Solution {
    pub fn merge_triplets(triplets: Vec<Vec<i32>>, target: Vec<i32>) -> bool {
        let [x, y, z]: [i32; 3] = target.try_into().unwrap();
        let (mut d, mut e, mut f) = (0, 0, 0);

        for triplet in triplets {
            if let [a, b, c] = triplet[..] {
                if a <= x && b <= y && c <= z {
                    d = d.max(a);
                    e = e.max(b);
                    f = f.max(c);
                }
            }
        }

        [d, e, f] == [x, y, z]
    }
}

// Accepted solution for LeetCode #1899: Merge Triplets to Form Target Triplet
function mergeTriplets(triplets: number[][], target: number[]): boolean {
    const [x, y, z] = target;
    let [d, e, f] = [0, 0, 0];
    for (const [a, b, c] of triplets) {
        if (a <= x && b <= y && c <= z) {
            d = Math.max(d, a);
            e = Math.max(e, b);
            f = Math.max(f, c);
        }
    }
    return d === x && e === y && f === z;
}

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 log n)

Space

O(1)

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.

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.