Approach

To effectively answer the question on implementing an algorithm to calculate the number of unique binary search trees (BSTs) that can be formed with 'n' distinct nodes, follow this structured framework:

1. Understand the Problem: Define what a binary search tree is and the concept of unique structures based on distinct nodes.

2. Identify the Mathematical Foundation: Recognize that the number of unique BSTs can be derived using the Catalan number formula.

3. Choose an Algorithmic Approach: Decide between a recursive solution, dynamic programming, or a combinatorial approach for optimal efficiency.

4. Implementation: Write the algorithm in a clear, logical manner, ensuring to handle edge cases.

Key Points

• Clarity on the Problem: Interviewers are looking for your understanding of BSTs and how unique trees can be formed based on node values.

• Mathematical Insight: Grasping the relationship to the Catalan numbers is crucial, as it demonstrates mathematical reasoning.

• Algorithm Choice: Discuss why you chose a specific algorithm (recursive vs. dynamic programming) and its implications on time and space complexity.

• Code Quality: Ensure your code is clean, well-commented, and follows best practices.

Standard Response

Sample Answer:

To implement an algorithm for calculating the number of unique binary search trees that can be formed with 'n' distinct nodes, we can utilize the mathematical concept of Catalan numbers. The nth Catalan number can be computed using the formula:

\[ C(n) = \frac{1}{n+1} \binom{2n}{n} \]

This can be expressed in a recursive manner, or we can use dynamic programming for efficiency.

Recursive Approach

• Understanding the Recursive Formula:

The number of unique BSTs formed with 'n' nodes can be calculated as:

\[
G(n) = \sum_{i=0}^{n-1} G(i) \times G(n-i-1)
\]

Here, \( G(i) \) represents the number of unique BSTs with 'i' nodes, and \( G(n-i-1) \) represents the unique BSTs formed by the remaining nodes.

• Base Cases:

• \( G(0) = 1 \) (an empty tree)

• \( G(1) = 1 \) (a single node tree)

• Algorithm Implementation (Recursive):

Dynamic Programming Approach

Using dynamic programming enhances efficiency by storing previously computed results:

• DP Array Definition: Define an array dp where dp[i] stores the number of unique BSTs that can be formed with 'i' nodes.

• Filling the DP Array:

• Initialize dp[0] and dp[1] to 1.

• Iterate from 2 to n, applying the recursive formula iteratively.

• Algorithm Implementation (Dynamic Programming):

This dynamic programming solution runs in O(n^2) time complexity and uses O(n) space.

Tips & Variations

Common Mistakes to Avoid:

• Ignoring Base Cases: Failing to define base cases can lead to incorrect results or infinite recursion.

• Inefficient Recursion: Not using memoization can cause exponential time complexity in a purely recursive solution.

Alternative Ways to Answer:

• Catalan Number Formula: Instead of implementing the recursive or DP approach, you can directly calculate the nth Catalan number using combinatorial methods.

Role-Specific Variations:

• For Technical Roles: Emphasize understanding the algorithm's complexity and trade-offs between recursion