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How to Solve Minimize the Maximum Edge Weight of Graph Problem
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Start Free for WindowsMedium#3419
LeetCode ProblemMinimize the Maximum Edge Weight of Graph
You are given two integers, n and threshold, as well as a directed weighted graph of n nodes numbered from 0 to n - 1. The graph is represented by a 2D integer array edges, where edges[i] = [Ai, Bi, Wi] indicates that there is an edge going from node Ai to node Bi with weight Wi. You have to remove some edges from this graph (possibly none), so that it satisfies the following conditions: Return the minimum possible value of the maximum edge weight after removing the necessary edges. If it is impossible for all conditions to be satisfied, return -1.
Binary SearchDepth-First SearchBreadth-First SearchGraphShortest Path
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Problem Breakdown
Understanding the Minimize the Maximum Edge Weight of Graph Problem
Let's break down this LeetCode problem and understand what makes it challenging in interview settings.
Problem Statement
You are given two integers, n and threshold, as well as a directed weighted graph of n nodes numbered from 0 to n - 1. The graph is represented by a 2D integer array edges, where edges[i] = [Ai, Bi, Wi] indicates that there is an edge going from node Ai to node Bi with weight Wi. You have to remove some edges from this graph (possibly none), so that it satisfies the following conditions: Return the minimum possible value of the maximum edge weight after removing the necessary edges. If it is impossible for all conditions to be satisfied, return -1.
MediumProblem #3419
LeetCodeMinimize the Maximum Edge Weight of Graph
Related Topics
Binary SearchDepth-First SearchBreadth-First SearchGraphShortest Path
How Phantom Code Helps
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Examples
# Example 1
Input
n = 5, edges = [[1,0,1],[2,0,2],[3,0,1],[4,3,1],[2,1,1]], threshold = 2
Output
1
# Example 2
Input
n = 5, edges = [[0,1,1],[0,2,2],[0,3,1],[0,4,1],[1,2,1],[1,4,1]], threshold = 1
Output
-1
# Example 3
Input
n = 5, edges = [[1,2,1],[1,3,3],[1,4,5],[2,3,2],[3,4,2],[4,0,1]], threshold = 1
Output
2
# Example 4
Input
n = 5, edges = [[1,2,1],[1,3,3],[1,4,5],[2,3,2],[4,0,1]], threshold = 1
Output
-1
Constraints
2 <= n <= 105
1 <= threshold <= n - 1
1 <= edges.length <= min(105, n * (n - 1) / 2).
edges[i].length == 3
0 <= Ai, Bi < n
Ai != Bi
1 <= Wi <= 106
There may be multiple edges between a pair of nodes, but they must have unique weights.
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Solve Minimize the Maximum Edge Weight of Graph — You are given two integers, n and threshold, as well as a directed weighted grap...
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def solve(input):
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Frequently Asked Questions
Common questions about solving Minimize the Maximum Edge Weight of Graph and using Phantom Code during coding interviews.
How does Phantom Code help me solve coding problems like Minimize the Maximum Edge Weight of Graph 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 Minimize the Maximum Edge Weight of Graph?
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.
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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 Minimize the Maximum Edge Weight of Graph 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 Minimize the Maximum Edge Weight of Graph 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.
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How does Phantom Code help when I'm struggling with Minimize the Maximum Edge Weight of Graph 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.
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