LeetCode #2818 — HARD

Apply Operations to Maximize Score

Break down a hard problem into reliable checkpoints, edge-case handling, and complexity trade-offs.

Solve on LeetCode
The Problem

Problem Statement

You are given an array nums of n positive integers and an integer k.

Initially, you start with a score of 1. You have to maximize your score by applying the following operation at most k times:

  • Choose any non-empty subarray nums[l, ..., r] that you haven't chosen previously.
  • Choose an element x of nums[l, ..., r] with the highest prime score. If multiple such elements exist, choose the one with the smallest index.
  • Multiply your score by x.

Here, nums[l, ..., r] denotes the subarray of nums starting at index l and ending at the index r, both ends being inclusive.

The prime score of an integer x is equal to the number of distinct prime factors of x. For example, the prime score of 300 is 3 since 300 = 2 * 2 * 3 * 5 * 5.

Return the maximum possible score after applying at most k operations.

Since the answer may be large, return it modulo 109 + 7.

Example 1:

Input: nums = [8,3,9,3,8], k = 2
Output: 81
Explanation: To get a score of 81, we can apply the following operations:
- Choose subarray nums[2, ..., 2]. nums[2] is the only element in this subarray. Hence, we multiply the score by nums[2]. The score becomes 1 * 9 = 9.
- Choose subarray nums[2, ..., 3]. Both nums[2] and nums[3] have a prime score of 1, but nums[2] has the smaller index. Hence, we multiply the score by nums[2]. The score becomes 9 * 9 = 81.
It can be proven that 81 is the highest score one can obtain.

Example 2:

Input: nums = [19,12,14,6,10,18], k = 3
Output: 4788
Explanation: To get a score of 4788, we can apply the following operations: 
- Choose subarray nums[0, ..., 0]. nums[0] is the only element in this subarray. Hence, we multiply the score by nums[0]. The score becomes 1 * 19 = 19.
- Choose subarray nums[5, ..., 5]. nums[5] is the only element in this subarray. Hence, we multiply the score by nums[5]. The score becomes 19 * 18 = 342.
- Choose subarray nums[2, ..., 3]. Both nums[2] and nums[3] have a prime score of 2, but nums[2] has the smaller index. Hence, we multipy the score by nums[2]. The score becomes 342 * 14 = 4788.
It can be proven that 4788 is the highest score one can obtain.

Constraints:

  • 1 <= nums.length == n <= 105
  • 1 <= nums[i] <= 105
  • 1 <= k <= min(n * (n + 1) / 2, 109)
Patterns Used

Roadmap

  1. Brute Force Baseline
  2. Core Insight
  3. Algorithm Walkthrough
  4. Edge Cases
  5. Full Annotated Code
  6. Interactive Study Demo
  7. Complexity Analysis
Step 01

Brute Force Baseline

Problem summary: You are given an array nums of n positive integers and an integer k. Initially, you start with a score of 1. You have to maximize your score by applying the following operation at most k times: Choose any non-empty subarray nums[l, ..., r] that you haven't chosen previously. Choose an element x of nums[l, ..., r] with the highest prime score. If multiple such elements exist, choose the one with the smallest index. Multiply your score by x. Here, nums[l, ..., r] denotes the subarray of nums starting at index l and ending at the index r, both ends being inclusive. The prime score of an integer x is equal to the number of distinct prime factors of x. For example, the prime score of 300 is 3 since 300 = 2 * 2 * 3 * 5 * 5. Return the maximum possible score after applying at most k operations. Since the answer may be large, return it modulo 109 + 7.

Baseline thinking

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

Pattern signal: Array · Math · Stack · Greedy

Example 1

[8,3,9,3,8]
2

Example 2

[19,12,14,6,10,18]
3

Related Problems

  • Next Greater Element IV (next-greater-element-iv)
Step 02

Core Insight

What unlocks the optimal approach

  • <div class="_1l1MA">Calculate <code>nums[i]</code>'s prime score <code>s[i]</code> by factoring in <code>O(sqrt(nums[i]))</code> time.</div>
  • <div class="_1l1MA">For each <code>nums[i]</code>, find the nearest index <code>left[i]</code> on the left (if any) such that <code>s[left[i]] >= s[i]</code>. if none is found, set <code>left[i]</code> to <code>-1</code>. Similarly, find the nearest index <code>right[i]</code> on the right (if any) such that <code>s[right[i]] > s[i]</code>. If none is found, set <code>right[i]</code> to <code>n</code>.</div>
  • <div class="_1l1MA">Use a monotonic stack to compute <code>right[i]</code> and <code>left[i]</code>.</div>
  • <div class="_1l1MA">For each index <code>i</code>, if <code>left[i] + 1 <= l <= i <= r <= right[i] - 1</code>, then <code>s[i]</code> is the maximum value in the range <code>[l, r]</code>. For this particular <code>i</code>, there are <code>ranges[i] = (i - left[i]) * (right[i] - i)</code> ranges where index <code>i</code> will be chosen.</div>
  • <div class="_1l1MA">Loop over all elements of <code>nums</code> by non-increasing prime score, each element will be chosen <code>min(ranges[i], remainingK)</code> times, where <code>reaminingK</code> denotes the number of remaining operations. Therefore, the score will be multiplied by <code>s[i]^min(ranges[i],remainingK)</code>.</div>
  • <div class="_1l1MA">Use fast exponentiation to quickly calculate <code>A^B mod C</code>.</div>
Interview move: turn each hint into an invariant you can check after every iteration/recursion step.
Step 03

Algorithm Walkthrough

Iteration Checklist

  1. Define state (indices, window, stack, map, DP cell, or recursion frame).
  2. Apply one transition step and update the invariant.
  3. Record answer candidate when condition is met.
  4. 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
Largest constraint values
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 #2818: Apply Operations to Maximize Score
class Solution {
    private final int mod = (int) 1e9 + 7;

    public int maximumScore(List<Integer> nums, int k) {
        int n = nums.size();
        int[][] arr = new int[n][0];
        for (int i = 0; i < n; ++i) {
            arr[i] = new int[] {i, primeFactors(nums.get(i)), nums.get(i)};
        }
        int[] left = new int[n];
        int[] right = new int[n];
        Arrays.fill(left, -1);
        Arrays.fill(right, n);
        Deque<Integer> stk = new ArrayDeque<>();
        for (int[] e : arr) {
            int i = e[0], f = e[1];
            while (!stk.isEmpty() && arr[stk.peek()][1] < f) {
                stk.pop();
            }
            if (!stk.isEmpty()) {
                left[i] = stk.peek();
            }
            stk.push(i);
        }
        stk.clear();
        for (int i = n - 1; i >= 0; --i) {
            int f = arr[i][1];
            while (!stk.isEmpty() && arr[stk.peek()][1] <= f) {
                stk.pop();
            }
            if (!stk.isEmpty()) {
                right[i] = stk.peek();
            }
            stk.push(i);
        }
        Arrays.sort(arr, (a, b) -> b[2] - a[2]);
        long ans = 1;
        for (int[] e : arr) {
            int i = e[0], x = e[2];
            int l = left[i], r = right[i];
            long cnt = (long) (i - l) * (r - i);
            if (cnt <= k) {
                ans = ans * qpow(x, cnt) % mod;
                k -= cnt;
            } else {
                ans = ans * qpow(x, k) % mod;
                break;
            }
        }
        return (int) ans;
    }

    private int primeFactors(int n) {
        int i = 2;
        Set<Integer> ans = new HashSet<>();
        while (i <= n / i) {
            while (n % i == 0) {
                ans.add(i);
                n /= i;
            }
            ++i;
        }
        if (n > 1) {
            ans.add(n);
        }
        return ans.size();
    }

    private int qpow(long a, long n) {
        long ans = 1;
        for (; n > 0; n >>= 1) {
            if ((n & 1) == 1) {
                ans = ans * a % mod;
            }
            a = a * a % mod;
        }
        return (int) ans;
    }
}
Step 06

Interactive Study Demo

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

Press Step or Run All to begin.
Step 07

Complexity Analysis

Time
O(n × log 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.

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.

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.