887. Super Egg Drop
hardWorst-case minimum drops to find the critical floor using k eggs and n floors. Classic combinatorial DP — invert the recurrence to think in floors-per-moves instead.
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Problem
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 uncracked 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.
Constraints
1 <= k <= 1001 <= n <= 10^4
Examples
Example 1
k = 1, n = 22Explanation: 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
k = 2, n = 63Example 3
k = 3, n = 144Solve it now
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Hints
Progressive — try the first before opening the next.
Hint 1
Naive: dp[k][n] = 1 + min over x of max(dp[k-1][x-1], dp[k][n-x]). O(k * n^2) — too slow.
Hint 2
Invert: let f(k, m) = max floors solvable with k eggs and m moves. Recurrence f(k, m) = f(k-1, m-1) + f(k, m-1) + 1.
Hint 3
Iterate m upward until f(k, m) >= n. Answer is that m.
Solution approach
Reveal approach
Invert the question. Instead of asking 'min moves for n floors with k eggs', ask 'max floors I can certify with k eggs and m moves' — call this f(k, m). The recurrence is f(k, m) = f(k-1, m-1) + f(k, m-1) + 1: on one move you drop an egg; if it breaks you have k-1 eggs and m-1 moves to certify the floors below; if it survives you have k eggs and m-1 moves to certify the floors above; plus the floor you just tested. Initialize f(*, 0) = 0 and f(0, *) = 0. Increment m starting at 1, computing all f(k', m) for k' from 1 to k, until f(k, m) >= n. Return m. O(k * m) where m is at most about log2(n) * k bounded — comfortably fits the constraints. Space O(k) using rolling rows.
Complexity
- Time
- O(k * log n)
- Space
- O(k)
Related patterns
- dynamic-programming
- binary-search
Related problems
Asked at
Companies reported asking this problem (sourced from public Glassdoor, Blind, and Levels.fyi interview posts).
- Amazon
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