LeetCode #2216 — MEDIUM

Minimum Deletions to Make Array Beautiful

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

The Problem

Problem Statement

You are given a 0-indexed integer array nums. The array nums is beautiful if:

nums.length is even.
nums[i] != nums[i + 1] for all i % 2 == 0.

Note that an empty array is considered beautiful.

You can delete any number of elements from nums. When you delete an element, all the elements to the right of the deleted element will be shifted one unit to the left to fill the gap created and all the elements to the left of the deleted element will remain unchanged.

Return the minimum number of elements to delete from nums to make it beautiful.

Example 1:

Input: nums = [1,1,2,3,5]
Output: 1
Explanation: You can delete either nums[0] or nums[1] to make nums = [1,2,3,5] which is beautiful. It can be proven you need at least 1 deletion to make nums beautiful.

Example 2:

Input: nums = [1,1,2,2,3,3]
Output: 2
Explanation: You can delete nums[0] and nums[5] to make nums = [1,2,2,3] which is beautiful. It can be proven you need at least 2 deletions to make nums beautiful.

Constraints:

1 <= nums.length <= 10⁵
0 <= nums[i] <= 10⁵

Step 01

Brute Force Baseline

Problem summary: You are given a 0-indexed integer array nums. The array nums is beautiful if: nums.length is even. nums[i] != nums[i + 1] for all i % 2 == 0. Note that an empty array is considered beautiful. You can delete any number of elements from nums. When you delete an element, all the elements to the right of the deleted element will be shifted one unit to the left to fill the gap created and all the elements to the left of the deleted element will remain unchanged. Return the minimum number of elements to delete from nums to make it beautiful.

Baseline thinking

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

Pattern signal: Array · Stack · Greedy

Example 1

[1,1,2,3,5]

Example 2

[1,1,2,2,3,3]

Core Insight

What unlocks the optimal approach

Delete as many adjacent equal elements as necessary.
If the length of nums is odd after the entire process, delete the last element.

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 #2216: Minimum Deletions to Make Array Beautiful
class Solution {
    public int minDeletion(int[] nums) {
        int n = nums.length;
        int ans = 0;
        for (int i = 0; i < n - 1; ++i) {
            if (nums[i] == nums[i + 1]) {
                ++ans;
            } else {
                ++i;
            }
        }
        ans += (n - ans) % 2;
        return ans;
    }
}

// Accepted solution for LeetCode #2216: Minimum Deletions to Make Array Beautiful
func minDeletion(nums []int) (ans int) {
	n := len(nums)
	for i := 0; i < n-1; i++ {
		if nums[i] == nums[i+1] {
			ans++
		} else {
			i++
		}
	}
	ans += (n - ans) % 2
	return
}

# Accepted solution for LeetCode #2216: Minimum Deletions to Make Array Beautiful
class Solution:
    def minDeletion(self, nums: List[int]) -> int:
        n = len(nums)
        i = ans = 0
        while i < n - 1:
            if nums[i] == nums[i + 1]:
                ans += 1
                i += 1
            else:
                i += 2
        ans += (n - ans) % 2
        return ans

// Accepted solution for LeetCode #2216: Minimum Deletions to Make Array Beautiful
impl Solution {
    pub fn min_deletion(nums: Vec<i32>) -> i32 {
        let n = nums.len();
        let mut ans = 0;
        let mut i = 0;
        while i < n - 1 {
            if nums[i] == nums[i + 1] {
                ans += 1;
                i += 1;
            } else {
                i += 2;
            }
        }
        ans += (n - ans) % 2;
        ans as i32
    }
}

// Accepted solution for LeetCode #2216: Minimum Deletions to Make Array Beautiful
function minDeletion(nums: number[]): number {
    const n = nums.length;
    let ans = 0;
    for (let i = 0; i < n - 1; ++i) {
        if (nums[i] === nums[i + 1]) {
            ++ans;
        } else {
            ++i;
        }
    }
    ans += (n - ans) % 2;
    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

BRUTE FORCE

O(n²) time

O(1) space

For each element, scan left (or right) to find the next greater/smaller element. The inner scan can visit up to n elements per outer iteration, giving O(n²) total comparisons. No extra space needed beyond loop variables.

MONOTONIC STACK

O(n) time

O(n) space

Each element is pushed onto the stack at most once and popped at most once, giving 2n total operations = O(n). The stack itself holds at most n elements in the worst case. The key insight: amortized O(1) per element despite the inner while-loop.

Shortcut: Each element pushed once + popped once → O(n) amortized. The inner while-loop does not make it O(n²).

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.

Breaking monotonic invariant

Wrong move: Pushing without popping stale elements invalidates next-greater/next-smaller logic.

Usually fails on: Indices point to blocked elements and outputs shift.

Fix: Pop while invariant is violated before pushing current element.

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.