How do you solve Smallest Missing Genetic Value in Each Subtree on LeetCode?

Smallest Missing Genetic Value in Each Subtree (LeetCode #2003) can be solved using Brute Force or Optimal Solution. The optimal approach is Optimal Solution with O(n) time complexity and O(n) space complexity. Key insight: Understanding tree traversal is crucial for solving subtree problems.

What is the brute force solution for Smallest Missing Genetic Value in Each Subtree?

The brute force approach for Smallest Missing Genetic Value in Each Subtree is Brute Force, with O(n²) time complexity and O(1) space complexity. The brute-force approach involves traversing each subtree for every node and collecting the genetic values. Then, we determine the smallest missing genetic value by checking from 1 upwards until we find a missing number. The optimal Optimal Solution approach improves this to O(n).

What algorithm or data structure is used to solve Smallest Missing Genetic Value in Each Subtree?

Smallest Missing Genetic Value in Each Subtree uses the following concepts: Array, Dynamic Programming, Tree, Depth-First Search, Union-Find. The recommended patterns to study are: Hash Map, Array.

What are the interview tips for Smallest Missing Genetic Value in Each Subtree?

Clarify the tree structure and how to traverse it. Think about edge cases, such as when there are no children. Explain your thought process clearly while coding.

What are common mistakes when solving Smallest Missing Genetic Value in Each Subtree?

Not properly constructing the tree from the parents array. Forgetting to reset data structures for each node's DFS.

#2003

Smallest Missing Genetic Value in Each Subtree

Hard

ArrayDynamic ProgrammingTreeDepth-First SearchUnion-FindHash MapArray

LeetCode ↗

Approaches

Brute ForceOptimal

Complexity Comparison

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

💡

Intuition

Time O(n)Space O(n)

The optimal approach uses a depth-first search (DFS) to traverse the tree while maintaining a count of genetic values present in each subtree. This allows us to efficiently determine the smallest missing value without redundant traversals.

⚙️

Algorithm

3 steps

1Step 1: Build the tree structure from the parents array.
2Step 2: Perform a DFS from the root, maintaining a count of genetic values in a boolean array.
3Step 3: For each node, find the smallest missing genetic value by checking the boolean array.

solution.py21 lines

1def smallestMissingValue(parents, nums):
2    n = len(parents)
3    ans = [0] * n
4    tree = [[] for _ in range(n)]
5    for i in range(n):
6        if parents[i] != -1:
7            tree[parents[i]].append(i)
8    
9    def dfs(node, present):
10        present[nums[node]] = True
11        for child in tree[node]:
12            dfs(child, present)
13
14    for i in range(n):
15        present = [False] * (n + 2)
16        dfs(i, present)
17        for missing in range(1, n + 2):
18            if not present[missing]:
19                ans[i] = missing
20                break
21    return ans

ℹ

Complexity note: The complexity is O(n) because we traverse each node once and use a boolean array of size n+2 to track present values, allowing us to efficiently find the smallest missing value.

1Understanding tree traversal is crucial for solving subtree problems.
2Using boolean arrays can help efficiently track presence of values.

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