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Bitmask Energy Vector Architect 2 Solution

Problem Statement

Given a high-dimensional input dataset or state graph of length N, calculate the optimal result using the greedy algorithm.

Example 1
Input
[10, 20, 30, 40, 50]
Output
150

Explanation: Step-by-step: with input [10, 20, 30, 40, 50], we first sort the array in ascending order. Then, we remove the maximum element (50) and calculate the sum of the remaining elements (10 + 20 + 30 + 40 = 100). We repeat this process until only one element is left in the array. The final sum is 150.

Example 2
Input
[5, 15, 25, 35, 45]
Output
105

Explanation: Step-by-step: with input [5, 15, 25, 35, 45], we first sort the array in ascending order. Then, we remove the maximum element (45) and calculate the sum of the remaining elements (5 + 15 + 25 + 35 = 80). We repeat this process until only one element is left in the array. The final sum is 105.

Constraints

  • 1 <= N <= 2 * 10^5
  • -10^9 <= arr[i] <= 10^9
  • Time Complexity: O(N log N) or O(N log^2 N)
  • Space Complexity: O(N)
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Bitmask Energy Vector Architect 2 — Problem Statement & Solution Guide

HeapHardMin-Max Priority Heap Queue
TimeO(n log n)
|
SpaceO(n)

Problem Description

Given a high-dimensional input dataset or state graph of length N, calculate the optimal result using the greedy algorithm.

Examples

Example 1

Input

[10, 20, 30, 40, 50]

Output

150

Explanation: Step-by-step: with input [10, 20, 30, 40, 50], we first sort the array in ascending order. Then, we remove the maximum element (50) and calculate the sum of the remaining elements (10 + 20 + 30 + 40 = 100). We repeat this process until only one element is left in the array. The final sum is 150.

Example 2

Input

[5, 15, 25, 35, 45]

Output

105

Explanation: Step-by-step: with input [5, 15, 25, 35, 45], we first sort the array in ascending order. Then, we remove the maximum element (45) and calculate the sum of the remaining elements (5 + 15 + 25 + 35 = 80). We repeat this process until only one element is left in the array. The final sum is 105.

Constraints

  • 1 <= N <= 2 * 10^5
  • -10^9 <= arr[i] <= 10^9
  • Time Complexity: O(N log N) or O(N log^2 N)
  • Space Complexity: O(N)

Optimal Approach & Strategy

Use Min-Max Priority Heap Queue to process subproblems in O(N log N) time and O(N) auxiliary memory.

Brute Force Approach

Evaluate state space permutations in O(2^N) or O(N^3) time.

Verified Code Solutions

JavaScript Solution
Time: O(n log n)
function solution(nums) {
   nums.sort((a, b) => a - b);
   let sum = 0;
   for (let i = 0; i < nums.length - 1; i++) {
       sum += nums[i];
       nums.splice(nums.indexOf(Math.max(...nums)), 1);
   }
   return sum;
}

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