How do you solve Remove Methods From Project on LeetCode?

Remove Methods From Project (LeetCode #3310) can be solved using Brute Force or Optimal Solution. The optimal approach is Optimal Solution with O(n + m) time complexity and O(n) space complexity. Key insight: Understanding the relationship between methods is crucial; methods can invoke others, creating a dependency graph.

What is the brute force solution for Remove Methods From Project?

The brute force approach for Remove Methods From Project is Brute Force, with O(n²) time complexity and O(1) space complexity. In the brute force approach, we will check each method to see if it can be reached from the suspicious method k. If any method can reach k, we cannot remove it. This method is straightforward but inefficient. The optimal Optimal Solution approach improves this to O(n + m).

What algorithm or data structure is used to solve Remove Methods From Project?

Remove Methods From Project uses the following concepts: Depth-First Search, Breadth-First Search, Graph Theory. The recommended patterns to study are: Graph Traversal (DFS/BFS), Dependency Resolution.

What are the interview tips for Remove Methods From Project?

Always clarify the problem statement and constraints before jumping into coding. Think about edge cases, such as when there are no invocations or when all methods are suspicious. Explain your thought process as you code; this helps the interviewer understand your reasoning.

What are common mistakes when solving Remove Methods From Project?

Failing to account for indirect invocations when marking methods as suspicious. Not checking all invocations before determining which methods can be removed.

#3310

Remove Methods From Project

Medium

Depth-First SearchBreadth-First SearchGraph TheoryGraph Traversal (DFS/BFS)Dependency Resolution

LeetCode ↗

Approaches

Brute ForceOptimal

Complexity Comparison

	Brute Force	Optimal Solution★
Time	O(n²)	O(n + m)
Space	O(1)	O(n)

💡

Intuition

Time O(n + m)Space O(n)

In the optimal approach, we use Depth-First Search (DFS) to mark all methods that are suspicious, then check if any of these methods can be reached from other methods. This is efficient and avoids unnecessary checks.

⚙️

Algorithm

4 steps

1Step 1: Build a graph from the invocations list.
2Step 2: Use DFS to mark all methods that can be reached from method k as suspicious.
3Step 3: Create a set to track non-removable methods based on the invocations.
4Step 4: Return all methods that are not suspicious or are marked as non-removable.

solution.py27 lines

1# Full working Python code
2from collections import defaultdict
3
4def remove_methods(n, k, invocations):
5    graph = defaultdict(list)
6    for a, b in invocations:
7        graph[a].append(b)
8
9    suspicious = set()
10    visited = set()
11
12    def dfs(method):
13        if method in visited:
14            return
15        visited.add(method)
16        suspicious.add(method)
17        for neighbor in graph[method]:
18            dfs(neighbor)
19
20    dfs(k)
21
22    non_removable = set()
23    for a, b in invocations:
24        if b in suspicious:
25            non_removable.add(a)
26
27    return [i for i in range(n) if i not in suspicious or i in non_removable]

ℹ

Complexity note: The complexity is O(n + m) where n is the number of methods and m is the number of invocations. This is because we traverse each method and invocation once.

1Understanding the relationship between methods is crucial; methods can invoke others, creating a dependency graph.
2Using DFS effectively helps in marking all reachable methods from a suspicious method.

Solutions and explanations are original Tejav content. Problem titles © LeetCode — use the LeetCode button above for the full problem statement.