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Node Vault Tracker 7 Solution

Problem Statement

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

Example 1
Input
[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
Output
45

Explanation: Step-by-step: Given the input array [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], we first identify the numbers greater than K=5, which are 6, 7, 8, 9, and 10. Then, we sum these numbers to get the target tracker value, which is 45.

Example 2
Input
[1, 2, 3, 4, 5]
Output
15

Explanation: Step-by-step: Given the input array [1, 2, 3, 4, 5], we first identify the numbers greater than K=3, which are 4 and 5. Then, we sum these numbers to get the target tracker value, which is 9. However, since 4 and 5 are greater than K=3, we should include them in the sum, giving us a total of 9.

Constraints

  • 1 <= N <= 10^5
  • -10^4 <= metrics[i] <= 10^4
  • 1 <= K <= N
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Node Vault Tracker 7 — Problem Statement & Solution Guide

Binary TreesEasyMonotonic Stack
TimeO(N)
|
SpaceO(H)

Problem Description

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

DSA Pattern Breakdown

DSA Pattern Breakdown

"Node Vault Tracker 7"

easy

WHY DOES IT MATTER?

Aggregating information in a single pass avoids exponential blow‑up on large trees.

OPTIMIZATION CHALLENGE

The key is to reduce repeated subtree traversals to a single linear pass.

REAL-WORLD CONNECTION

Similar to summarizing sensor data in hierarchical IoT networks where each gateway aggregates child readings.

Cache child results in the call stack and compute parent values immediately to keep memory footprint minimal.

COMPLEXITY AT A GLANCE

⏱ Time:O(N)
💾 Space:O(H)

Core Theory — Why This Approach?

The Node Vault Tracker problem reduces to aggregating values across a binary tree where each node represents a vault with a metric. By performing a single post‑order traversal we can compute the required tracker value using a bottom‑up dynamic programming approach, storing only the necessary intermediate results. Naïve solutions often recompute sub‑tree aggregates for each node, leading to O(N^2) time on skewed trees because each subtree is visited repeatedly. The optimal paradigm leverages the tree’s hierarchical structure: each node’s answer is derived from its children’s answers in constant time, guaranteeing linear O(N) overall complexity.

Interview Questions on This Problem

Q1How does a post‑order traversal help compute aggregate values in a binary tree?

It visits children before the parent, ensuring that all sub‑tree information is available when processing a node. This eliminates redundant recomputation of child results.

Q2Why does a naïve double‑loop over nodes lead to O(N^2) time for this problem?

Each node may trigger a full traversal of its subtree, causing overlapping work across nodes. In the worst case (e.g., a linked‑list shaped tree) the total work sums to a quadratic series.

Q3What is the space complexity of the optimal solution and why?

It is O(H), where H is the tree height, because recursion (or an explicit stack) stores only the call chain. No additional data structures proportional to N are required.

Examples

Example 1

Input

[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]

Output

45

Explanation: Step-by-step: Given the input array [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], we first identify the numbers greater than K=5, which are 6, 7, 8, 9, and 10. Then, we sum these numbers to get the target tracker value, which is 45.

Example 2

Input

[1, 2, 3, 4, 5]

Output

15

Explanation: Step-by-step: Given the input array [1, 2, 3, 4, 5], we first identify the numbers greater than K=3, which are 4 and 5. Then, we sum these numbers to get the target tracker value, which is 9. However, since 4 and 5 are greater than K=3, we should include them in the sum, giving us a total of 9.

Constraints

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

Optimal Approach & Strategy

Perform a single DFS that returns the subtree aggregate to its parent, computing each node’s contribution in O(1) time.

Brute Force Approach

Re‑compute the subtree sum for every node by launching a fresh DFS from that node, leading to repeated work.

Verified Code Solutions

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

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