LeetCode #1770 — HARD

Maximum Score from Performing Multiplication Operations

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 two 0-indexed integer arrays nums and multipliers of size n and m respectively, where n >= m.

You begin with a score of 0. You want to perform exactly m operations. On the ith operation (0-indexed) you will:

  • Choose one integer x from either the start or the end of the array nums.
  • Add multipliers[i] * x to your score.
    • Note that multipliers[0] corresponds to the first operation, multipliers[1] to the second operation, and so on.
  • Remove x from nums.

Return the maximum score after performing m operations.

Example 1:

Input: nums = [1,2,3], multipliers = [3,2,1]
Output: 14
Explanation: An optimal solution is as follows:
- Choose from the end, [1,2,3], adding 3 * 3 = 9 to the score.
- Choose from the end, [1,2], adding 2 * 2 = 4 to the score.
- Choose from the end, [1], adding 1 * 1 = 1 to the score.
The total score is 9 + 4 + 1 = 14.

Example 2:

Input: nums = [-5,-3,-3,-2,7,1], multipliers = [-10,-5,3,4,6]
Output: 102
Explanation: An optimal solution is as follows:
- Choose from the start, [-5,-3,-3,-2,7,1], adding -5 * -10 = 50 to the score.
- Choose from the start, [-3,-3,-2,7,1], adding -3 * -5 = 15 to the score.
- Choose from the start, [-3,-2,7,1], adding -3 * 3 = -9 to the score.
- Choose from the end, [-2,7,1], adding 1 * 4 = 4 to the score.
- Choose from the end, [-2,7], adding 7 * 6 = 42 to the score. 
The total score is 50 + 15 - 9 + 4 + 42 = 102.

Constraints:

  • n == nums.length
  • m == multipliers.length
  • 1 <= m <= 300
  • m <= n <= 105
  • -1000 <= nums[i], multipliers[i] <= 1000
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 two 0-indexed integer arrays nums and multipliers of size n and m respectively, where n >= m. You begin with a score of 0. You want to perform exactly m operations. On the ith operation (0-indexed) you will: Choose one integer x from either the start or the end of the array nums. Add multipliers[i] * x to your score. Note that multipliers[0] corresponds to the first operation, multipliers[1] to the second operation, and so on. Remove x from nums. Return the maximum score after performing m operations.

Baseline thinking

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

Pattern signal: Array · Dynamic Programming

Example 1

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

Example 2

[-5,-3,-3,-2,7,1]
[-10,-5,3,4,6]

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Step 02

Core Insight

What unlocks the optimal approach

  • At first glance, the solution seems to be greedy, but if you try to greedily take the largest value from the beginning or the end, this will not be optimal.
  • You should try all scenarios but this will be costly.
  • Memoizing the pre-visited states while trying all the possible scenarios will reduce the complexity, and hence dp is a perfect choice here.
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 #1770: Maximum Score from Performing Multiplication Operations
class Solution {
    private Integer[][] f;
    private int[] multipliers;
    private int[] nums;
    private int n;
    private int m;

    public int maximumScore(int[] nums, int[] multipliers) {
        n = nums.length;
        m = multipliers.length;
        f = new Integer[m][m];
        this.nums = nums;
        this.multipliers = multipliers;
        return dfs(0, 0);
    }

    private int dfs(int i, int j) {
        if (i >= m || j >= m || (i + j) >= m) {
            return 0;
        }
        if (f[i][j] != null) {
            return f[i][j];
        }
        int k = i + j;
        int a = dfs(i + 1, j) + nums[i] * multipliers[k];
        int b = dfs(i, j + 1) + nums[n - 1 - j] * multipliers[k];
        f[i][j] = Math.max(a, b);
        return f[i][j];
    }
}
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 × m)
Space
O(n × m)

Approach Breakdown

RECURSIVE
O(2ⁿ) time
O(n) space

Pure recursion explores every possible choice at each step. With two choices per state (take or skip), the decision tree has 2ⁿ leaves. The recursion stack uses O(n) space. Many subproblems are recomputed exponentially many times.

DYNAMIC PROGRAMMING
O(n × m) time
O(n × m) space

Each cell in the DP table is computed exactly once from previously solved subproblems. The table dimensions determine both time and space. Look for the state variables — each unique combination of state values is one cell. Often a rolling array can reduce space by one dimension.

Shortcut: Count your DP state dimensions → that’s your time. Can you drop one? That’s your space optimization.
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.

State misses one required dimension

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.