Overflow in intermediate arithmetic
Wrong move: Temporary multiplications exceed integer bounds.
Usually fails on: Large inputs wrap around unexpectedly.
Fix: Use wider types, modular arithmetic, or rearranged operations.
Break down a hard problem into reliable checkpoints, edge-case handling, and complexity trade-offs.
You are given k identical eggs and you have access to a building with n floors labeled from 1 to n.
You know that there exists a floor f where 0 <= f <= n such that any egg dropped at a floor higher than f will break, and any egg dropped at or below floor f will not break.
Each move, you may take an unbroken egg and drop it from any floor x (where 1 <= x <= n). If the egg breaks, you can no longer use it. However, if the egg does not break, you may reuse it in future moves.
Return the minimum number of moves that you need to determine with certainty what the value of f is.
Example 1:
Input: k = 1, n = 2 Output: 2 Explanation: Drop the egg from floor 1. If it breaks, we know that f = 0. Otherwise, drop the egg from floor 2. If it breaks, we know that f = 1. If it does not break, then we know f = 2. Hence, we need at minimum 2 moves to determine with certainty what the value of f is.
Example 2:
Input: k = 2, n = 6 Output: 3
Example 3:
Input: k = 3, n = 14 Output: 4
Constraints:
1 <= k <= 1001 <= n <= 104Problem summary: You are given k identical eggs and you have access to a building with n floors labeled from 1 to n. You know that there exists a floor f where 0 <= f <= n such that any egg dropped at a floor higher than f will break, and any egg dropped at or below floor f will not break. Each move, you may take an unbroken egg and drop it from any floor x (where 1 <= x <= n). If the egg breaks, you can no longer use it. However, if the egg does not break, you may reuse it in future moves. Return the minimum number of moves that you need to determine with certainty what the value of f is.
Start with the most direct exhaustive search. That gives a correctness anchor before optimizing.
Pattern signal: Math · Binary Search · Dynamic Programming
1 2
2 6
3 14
egg-drop-with-2-eggs-and-n-floors)Source-backed implementations are provided below for direct study and interview prep.
// Accepted solution for LeetCode #887: Super Egg Drop
class Solution {
private int[][] f;
public int superEggDrop(int k, int n) {
f = new int[n + 1][k + 1];
return dfs(n, k);
}
private int dfs(int i, int j) {
if (i < 1) {
return 0;
}
if (j == 1) {
return i;
}
if (f[i][j] != 0) {
return f[i][j];
}
int l = 1, r = i;
while (l < r) {
int mid = (l + r + 1) >> 1;
int a = dfs(mid - 1, j - 1);
int b = dfs(i - mid, j);
if (a <= b) {
l = mid;
} else {
r = mid - 1;
}
}
return f[i][j] = Math.max(dfs(l - 1, j - 1), dfs(i - l, j)) + 1;
}
}
// Accepted solution for LeetCode #887: Super Egg Drop
func superEggDrop(k int, n int) int {
f := make([][]int, n+1)
for i := range f {
f[i] = make([]int, k+1)
}
var dfs func(i, j int) int
dfs = func(i, j int) int {
if i < 1 {
return 0
}
if j == 1 {
return i
}
if f[i][j] != 0 {
return f[i][j]
}
l, r := 1, i
for l < r {
mid := (l + r + 1) >> 1
a, b := dfs(mid-1, j-1), dfs(i-mid, j)
if a <= b {
l = mid
} else {
r = mid - 1
}
}
f[i][j] = max(dfs(l-1, j-1), dfs(i-l, j)) + 1
return f[i][j]
}
return dfs(n, k)
}
# Accepted solution for LeetCode #887: Super Egg Drop
class Solution:
def superEggDrop(self, k: int, n: int) -> int:
@cache
def dfs(i: int, j: int) -> int:
if i < 1:
return 0
if j == 1:
return i
l, r = 1, i
while l < r:
mid = (l + r + 1) >> 1
a = dfs(mid - 1, j - 1)
b = dfs(i - mid, j)
if a <= b:
l = mid
else:
r = mid - 1
return max(dfs(l - 1, j - 1), dfs(i - l, j)) + 1
return dfs(n, k)
// Accepted solution for LeetCode #887: Super Egg Drop
/**
* [0887] Super Egg Drop
*
* You are given k identical eggs and you have access to a building with n floors labeled from 1 to n.
* You know that there exists a floor f where 0 <= f <= n such that any egg dropped at a floor higher than f will break, and any egg dropped at or below floor f will not break.
* Each move, you may take an unbroken egg and drop it from any floor x (where 1 <= x <= n). If the egg breaks, you can no longer use it. However, if the egg does not break, you may reuse it in future moves.
* Return the minimum number of moves that you need to determine with certainty what the value of f is.
*
* Example 1:
*
* Input: k = 1, n = 2
* Output: 2
* Explanation:
* Drop the egg from floor 1. If it breaks, we know that f = 0.
* Otherwise, drop the egg from floor 2. If it breaks, we know that f = 1.
* If it does not break, then we know f = 2.
* Hence, we need at minimum 2 moves to determine with certainty what the value of f is.
*
* Example 2:
*
* Input: k = 2, n = 6
* Output: 3
*
* Example 3:
*
* Input: k = 3, n = 14
* Output: 4
*
*
* Constraints:
*
* 1 <= k <= 100
* 1 <= n <= 10^4
*
*/
pub struct Solution {}
// problem: https://leetcode.com/problems/super-egg-drop/
// discuss: https://leetcode.com/problems/super-egg-drop/discuss/?currentPage=1&orderBy=most_votes&query=
// submission codes start here
impl Solution {
// Credit: https://leetcode.com/problems/super-egg-drop/solutions/631518/rust-dp-0ms-100/
pub fn super_egg_drop(k: i32, n: i32) -> i32 {
let n = n as usize;
let mut dp = Vec::with_capacity(n + 1);
for i in 0..=n {
dp.push(i);
}
let mut last_dp = dp.clone();
for _ in 2..=k {
dp.clear();
dp.push(0);
for i in 1..=n {
if dp[i - 1] >= n {
break;
}
dp.push(1 + dp[i - 1] + last_dp[i - 1]);
}
last_dp[..dp.len()].copy_from_slice(&dp);
}
dp.len() as i32 - 1
}
}
// submission codes end
#[cfg(test)]
mod tests {
use super::*;
#[test]
fn test_0887_example_1() {
let k = 1;
let n = 2;
let result = 2;
assert_eq!(Solution::super_egg_drop(k, n), result);
}
#[test]
fn test_0887_example_2() {
let k = 2;
let n = 6;
let result = 3;
assert_eq!(Solution::super_egg_drop(k, n), result);
}
#[test]
fn test_0887_example_3() {
let k = 3;
let n = 14;
let result = 4;
assert_eq!(Solution::super_egg_drop(k, n), result);
}
}
// Accepted solution for LeetCode #887: Super Egg Drop
function superEggDrop(k: number, n: number): number {
const f: number[][] = new Array(n + 1).fill(0).map(() => new Array(k + 1).fill(0));
const dfs = (i: number, j: number): number => {
if (i < 1) {
return 0;
}
if (j === 1) {
return i;
}
if (f[i][j]) {
return f[i][j];
}
let l = 1;
let r = i;
while (l < r) {
const mid = (l + r + 1) >> 1;
const a = dfs(mid - 1, j - 1);
const b = dfs(i - mid, j);
if (a <= b) {
l = mid;
} else {
r = mid - 1;
}
}
return (f[i][j] = Math.max(dfs(l - 1, j - 1), dfs(i - l, j)) + 1);
};
return dfs(n, k);
}
Use this to step through a reusable interview workflow for this problem.
Check every element from left to right until we find the target or exhaust the array. Each comparison is O(1), and we may visit all n elements, giving O(n). No extra space needed.
Each comparison eliminates half the remaining search space. After k comparisons, the space is n/2ᵏ. We stop when the space is 1, so k = log₂ n. No extra memory needed — just two pointers (lo, hi).
Review these before coding to avoid predictable interview regressions.
Wrong move: Temporary multiplications exceed integer bounds.
Usually fails on: Large inputs wrap around unexpectedly.
Fix: Use wider types, modular arithmetic, or rearranged operations.
Wrong move: Setting `lo = mid` or `hi = mid` can stall and create an infinite loop.
Usually fails on: Two-element ranges never converge.
Fix: Use `lo = mid + 1` or `hi = mid - 1` where appropriate.
Wrong move: An incomplete state merges distinct subproblems and caches incorrect answers.
Usually fails on: Correctness breaks on cases that differ only in hidden state.
Fix: Define state so each unique subproblem maps to one DP cell.