What is the optimal time complexity of solving BST?

The optimized solution achieves a time complexity of O(n) by using sorting, mathematical shortcuts, or hash table lookups.

What is the auxiliary space complexity for BST?

The space complexity is O(1) since we only use a minimal/constant amount of extra memory for tracking variables (or auxiliary space if dynamic structures are allocated).

What are the typical edge cases we must handle in BST?

Key edge cases include empty collections, negative or large bounds causing integer overflow, arrays with only one element, or inputs where target criteria are not met.

How can we optimize the brute force approach for BST?

Brute force methods often inspect all possible combinations. We optimize this by sorting the input list to enable binary search, or tracking processed items in a set to avoid redundant nested loop lookups.

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DSA SectionEasy

BST

Learn how to solve the 'BST' problem. This detailed resource details brute force and optimized approaches.

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Problem Statement

Easy

Write a function is_valid_bst(tree_arr) that takes an array representation of a binary tree tree_arr (root at index 0, children of i at 2i+1 and 2i+2, with None representing empty nodes) and returns True if it is a valid Binary Search Tree (BST), or False otherwise.

Constraints

•0 <= len(tree_arr) <= 1000

Examples

Example 1

Input

tree_arr = [2, 1, 3]

Output

True

Explanation

The left child 1 is smaller than root 2, and right child 3 is greater than root 2.

Example 2

Input

tree_arr = [5, 1, 4, None, None, 3, 6]

Output

False

Explanation

The root value is 5, but its right child 4 contains a left child 3 which is smaller than 5.

Need a Hint?

Perform a recursive tree traversal (DFS) or level-order traversal (BFS) using a queue/stack.

Edge Cases to Watch

Empty list or null input variables
Single item lists/arrays
Extremely large input bounds causing integer or stack overflow

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