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How to Solve Maximum Weighted K-Edge Path Problem
Master the Maximum Weighted K-Edge Path LeetCode problem with undetectable real-time assistance. Get instant solutions and explanations during your coding interviews.
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Start Free for WindowsMedium#3543
LeetCode ProblemMaximum Weighted K-Edge Path
You are given an integer n and a Directed Acyclic Graph (DAG) with n nodes labeled from 0 to n - 1. This is represented by a 2D array edges, where edges[i] = [ui, vi, wi] indicates a directed edge from node ui to vi with weight wi. You are also given two integers, k and t. Your task is to determine the maximum possible sum of edge weights for any path in the graph such that: Return the maximum possible sum of weights for such a path. If no such path exists, return -1.
Hash TableDynamic ProgrammingGraph
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Problem Breakdown
Understanding the Maximum Weighted K-Edge Path Problem
Let's break down this LeetCode problem and understand what makes it challenging in interview settings.
Problem Statement
You are given an integer n and a Directed Acyclic Graph (DAG) with n nodes labeled from 0 to n - 1. This is represented by a 2D array edges, where edges[i] = [ui, vi, wi] indicates a directed edge from node ui to vi with weight wi. You are also given two integers, k and t. Your task is to determine the maximum possible sum of edge weights for any path in the graph such that: Return the maximum possible sum of weights for such a path. If no such path exists, return -1.
MediumProblem #3543
LeetCodeMaximum Weighted K-Edge Path
Related Topics
Hash TableDynamic ProgrammingGraph
How Phantom Code Helps
Get real-time assistance for Maximum Weighted K-Edge Path problems during coding interviews. Phantom Code provides instant solutions and explanations.
Examples
# Example 1
Input
n = 3, edges = [[0,1,1],[1,2,2]], k = 2, t = 4
Output
3
# Example 2
Input
n = 3, edges = [[0,1,2],[0,2,3]], k = 1, t = 3
Output
2
# Example 3
Input
n = 3, edges = [[0,1,6],[1,2,8]], k = 1, t = 6
Output
-1
Constraints
1 <= n <= 300
0 <= edges.length <= 300
edges[i] = [ui, vi, wi]
0 <= ui, vi < n
ui != vi
1 <= wi <= 10
0 <= k <= 300
1 <= t <= 600
The input graph is guaranteed to be a DAG.
There are no duplicate edges.
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Solve Maximum Weighted K-Edge Path — You are given an integer n and a Directed Acyclic Graph (DAG) with n nodes label...
Here's the optimal approach using Hash Table:
def solve(input):
# Optimal O(n) solution
return result
Time: O(n) | Space: O(n)
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Frequently Asked Questions
Common questions about solving Maximum Weighted K-Edge Path and using Phantom Code during coding interviews.
How does Phantom Code help me solve coding problems like Maximum Weighted K-Edge Path during interviews?
Phantom Code generates complete solutions instantly with proper complexity analysis, letting you focus on explaining your approach and demonstrating problem-solving skills rather than getting stuck on implementation details during high-pressure situations.
What if the interviewer asks follow-up questions or modifications to problems like Maximum Weighted K-Edge Path?
Phantom Code adapts in real-time. Screenshot the modified problem or use audio mode to capture the interviewer's follow-up, and get an updated solution within seconds — including edge cases and optimizations.
Can Phantom Code help me communicate my solution for problems like Maximum Weighted K-Edge Path?
Yes. Phantom Code provides step-by-step approach explanations with 3 progressive thoughts, so you can walk through your solution naturally. It also provides time and space complexity analysis to discuss trade-offs.
How does Phantom Code assist with different programming languages for Maximum Weighted K-Edge Path type problems?
Phantom Code supports 11 programming languages including Python, Java, C++, JavaScript, TypeScript, Go, Rust, Ruby, Swift, Kotlin, and C#. Set your preferred language and get idiomatic solutions.
What coding mistakes does Phantom Code help me avoid during Maximum Weighted K-Edge Path interviews?
Phantom Code catches common mistakes like off-by-one errors, missing edge cases, incorrect base conditions, and suboptimal approaches. It provides clean, well-structured code that handles all edge cases.
Is Phantom Code detectable when solving problems like Maximum Weighted K-Edge Path during live interviews?
No. Phantom Code runs as an invisible desktop overlay that doesn't appear in screen shares, recordings, or proctoring software. It works with Zoom, HackerRank, CodeSignal, CoderPad, and all major platforms.
How does Phantom Code help when I'm struggling with Maximum Weighted K-Edge Path style questions under pressure?
The AI provides structured guidance: first the approach (what algorithm/data structure to use and why), then the implementation with clean code, and finally complexity analysis. This helps you think clearly even under pressure.
Does Phantom Code work for the coding platforms used in Maximum Weighted K-Edge Path interviews?
Yes. Phantom Code works with all major coding platforms including LeetCode, HackerRank, CodeSignal, CoderPad, HackerEarth, and any browser-based coding environment.
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