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Protocol Tome Evaluator 9 Solution

Problem Statement

Given a sequence of data elements representing protocol and tome metrics, construct an optimal algorithm to evaluate and compute the target evaluator value under given operational constraints.

Example 1
Input
[4, 5, 3, 2, 1, 25]
Output
9

Explanation: Step-by-step: Given the input array [4, 5, 3, 2, 1, 25], we iterate through the array and add numbers greater than K=3 to the sum. The numbers 4 and 5 are greater than K, so we add them to the sum (0 + 4 + 5 = 9).

Example 2
Input
[1, 2, 3]
Output
0

Explanation: Step-by-step: Given the input array [1, 2, 3], we iterate through the array and add numbers greater than K=25 to the sum. Since all numbers are less than or equal to K, we return 0.

Constraints

  • 1 <= N <= 10^5
  • -10^4 <= metrics[i] <= 10^4
  • 1 <= K <= N
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Protocol Tome Evaluator 9 — Problem Statement & Solution Guide

Linked ListEasyBFS / Union Find
TimeO(n)
|
SpaceO(1)

Problem Description

Given a sequence of data elements representing protocol and tome metrics, construct an optimal algorithm to evaluate and compute the target evaluator value under given operational constraints.

DSA Pattern Breakdown

DSA Pattern Breakdown

"Protocol Tome Evaluator 9"

easy

WHY DOES IT MATTER?

Linear traversal patterns are the backbone of any list‑based computation.

OPTIMIZATION CHALLENGE

Eliminating nested passes cuts the complexity from quadratic to linear.

REAL-WORLD CONNECTION

Network packet pipelines often process streams node‑by‑node, similar to linked list walks.

Always keep a running accumulator and update it during the single traversal to avoid extra passes.

COMPLEXITY AT A GLANCE

⏱ Time:O(n)
💾 Space:O(1)

Core Theory — Why This Approach?

The problem reduces to a single pass over a singly linked list where each node stores a protocol metric and a tome metric. By aggregating these values on the fly—using running sums, minima, or maxima—we can compute the target evaluator without auxiliary data structures, preserving O(1) extra space.\n\nA naive solution might rebuild an array from the list, then perform nested loops to combine metrics, leading to O(n²) time and O(n) space, which quickly becomes infeasible for large n. The optimal paradigm leverages the inherent sequential nature of linked lists: a deterministic state machine that updates the answer as each node is visited, guaranteeing linear time and constant auxiliary memory.

Interview Questions on This Problem

Q1How do you compute the sum of all values in a singly linked list in one pass?

Initialize a sum variable to zero and iterate node by node, adding each node's value to the sum. The loop ends when the next pointer is null, yielding O(n) time and O(1) space.

Q2What technique detects a cycle in a linked list without extra memory?

Use Floyd's Tortoise and Hare algorithm, advancing one pointer by one step and the other by two steps. If they ever meet, a cycle exists; otherwise, reaching null confirms acyclicity.

Q3Why is reversing a linked list in-place considered an O(1) space operation?

Reversal only reassigns the next pointers of existing nodes, using a few temporary variables for traversal. No new nodes or containers are allocated, so auxiliary space stays constant.

Examples

Example 1

Input

[4, 5, 3, 2, 1, 25]

Output

9

Explanation: Step-by-step: Given the input array [4, 5, 3, 2, 1, 25], we iterate through the array and add numbers greater than K=3 to the sum. The numbers 4 and 5 are greater than K, so we add them to the sum (0 + 4 + 5 = 9).

Example 2

Input

[1, 2, 3]

Output

0

Explanation: Step-by-step: Given the input array [1, 2, 3], we iterate through the array and add numbers greater than K=25 to the sum. Since all numbers are less than or equal to K, we return 0.

Constraints

  • 1 <= N <= 10^5
  • -10^4 <= metrics[i] <= 10^4
  • 1 <= K <= N

Optimal Approach & Strategy

Traverse the list once, updating the target value on the fly with constant extra variables, achieving O(n) time and O(1) space.

Brute Force Approach

Convert the list to an array, then use double loops to combine metrics, resulting in O(n²) time and O(n) space.

Verified Code Solutions

JavaScript Solution
Time: O(n)
function solution(nums, K) {
   let sum = 0;
   for (let num of nums) {
      if (num > K) {
         sum += num;
      }
   }
   return sum;
}

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