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.
Break down a hard problem into reliable checkpoints, edge-case handling, and complexity trade-offs.
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:
x from either the start or the end of the array nums.multipliers[i] * x to your score.
multipliers[0] corresponds to the first operation, multipliers[1] to the second operation, and so on.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.lengthm == multipliers.length1 <= m <= 300m <= n <= 105 -1000 <= nums[i], multipliers[i] <= 1000Problem 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.
Start with the most direct exhaustive search. That gives a correctness anchor before optimizing.
Pattern signal: Array · Dynamic Programming
[1,2,3] [3,2,1]
[-5,-3,-3,-2,7,1] [-10,-5,3,4,6]
maximum-points-you-can-obtain-from-cards)stone-game-vii)maximum-spending-after-buying-items)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];
}
}
// Accepted solution for LeetCode #1770: Maximum Score from Performing Multiplication Operations
func maximumScore(nums []int, multipliers []int) int {
n, m := len(nums), len(multipliers)
f := make([][]int, m)
for i := range f {
f[i] = make([]int, m)
for j := range f[i] {
f[i][j] = 1 << 30
}
}
var dfs func(i, j int) int
dfs = func(i, j int) int {
if i >= m || j >= m || i+j >= m {
return 0
}
if f[i][j] != 1<<30 {
return f[i][j]
}
k := i + j
a := dfs(i+1, j) + nums[i]*multipliers[k]
b := dfs(i, j+1) + nums[n-j-1]*multipliers[k]
f[i][j] = max(a, b)
return f[i][j]
}
return dfs(0, 0)
}
# Accepted solution for LeetCode #1770: Maximum Score from Performing Multiplication Operations
class Solution:
def maximumScore(self, nums: List[int], multipliers: List[int]) -> int:
@cache
def f(i, j, k):
if k >= m or i >= n or j < 0:
return 0
a = f(i + 1, j, k + 1) + nums[i] * multipliers[k]
b = f(i, j - 1, k + 1) + nums[j] * multipliers[k]
return max(a, b)
n = len(nums)
m = len(multipliers)
return f(0, n - 1, 0)
// Accepted solution for LeetCode #1770: Maximum Score from Performing Multiplication Operations
/**
* [1770] Maximum Score from Performing Multiplication Operations
*
* 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 i^th 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,<u>3</u>], adding 3 * 3 = 9 to the score.
* - Choose from the end, [1,<u>2</u>], adding 2 * 2 = 4 to the score.
* - Choose from the end, [<u>1</u>], 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, [<u>-5</u>,-3,-3,-2,7,1], adding -5 * -10 = 50 to the score.
* - Choose from the start, [<u>-3</u>,-3,-2,7,1], adding -3 * -5 = 15 to the score.
* - Choose from the start, [<u>-3</u>,-2,7,1], adding -3 * 3 = -9 to the score.
* - Choose from the end, [-2,7,<u>1</u>], adding 1 * 4 = 4 to the score.
* - Choose from the end, [-2,<u>7</u>], 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 <= 10^5
* -1000 <= nums[i], multipliers[i] <= 1000
*
*/
pub struct Solution {}
// problem: https://leetcode.com/problems/maximum-score-from-performing-multiplication-operations/
// discuss: https://leetcode.com/problems/maximum-score-from-performing-multiplication-operations/discuss/?currentPage=1&orderBy=most_votes&query=
// submission codes start here
impl Solution {
// Credit: https://leetcode.com/problems/maximum-score-from-performing-multiplication-operations/solutions/2455163/rust-bottom-up-and-top-down-dp-with-comments/
pub fn maximum_score(nums: Vec<i32>, multipliers: Vec<i32>) -> i32 {
let (n, m) = (nums.len(), multipliers.len());
let mut dp = vec![vec![0; m + 1]; m + 1];
for left in (0..m).rev() {
for right in (0..m - left).rev() {
let multiplier = multipliers[left + right];
dp[left][right] = (dp[left + 1][right] + multiplier * nums[left])
.max(dp[left][right + 1] + multiplier * nums[n - right - 1]);
}
}
dp[0][0]
}
}
// submission codes end
#[cfg(test)]
mod tests {
use super::*;
#[test]
fn test_1770_example_1() {
let nums = vec![1, 2, 3];
let multipliers = vec![3, 2, 1];
let result = 14;
assert_eq!(Solution::maximum_score(nums, multipliers), result);
}
#[test]
fn test_1770_example_2() {
let nums = vec![-5, -3, -3, -2, 7, 1];
let multipliers = vec![-10, -5, 3, 4, 6];
let result = 102;
assert_eq!(Solution::maximum_score(nums, multipliers), result);
}
}
// Accepted solution for LeetCode #1770: Maximum Score from Performing Multiplication Operations
function maximumScore(nums: number[], multipliers: number[]): number {
const inf = 1 << 30;
const n = nums.length;
const m = multipliers.length;
const f = new Array(m + 1).fill(0).map(() => new Array(m + 1).fill(-inf));
f[0][0] = 0;
let ans = -inf;
for (let i = 0; i <= m; ++i) {
for (let j = 0; j <= m - i; ++j) {
const k = i + j - 1;
if (i > 0) {
f[i][j] = Math.max(f[i][j], f[i - 1][j] + nums[i - 1] * multipliers[k]);
}
if (j > 0) {
f[i][j] = Math.max(f[i][j], f[i][j - 1] + nums[n - j] * multipliers[k]);
}
if (i + j === m) {
ans = Math.max(ans, f[i][j]);
}
}
}
return ans;
}
Use this to step through a reusable interview workflow for this problem.
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.
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.
Review these before coding to avoid predictable interview regressions.
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.
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.