Approach

To effectively answer the question about implementing an algorithm to determine the longest palindromic subsequence in a given string, follow a structured framework that demonstrates both your understanding of the problem and your coding skills.

1. Understand the Problem: Define what a palindromic subsequence is and why it matters.

2. Choose the Right Algorithm: Discuss potential algorithms, focusing on dynamic programming as a suitable approach.

3. Explain the Steps: Outline the steps needed to implement the algorithm, including initialization, state transition, and final result extraction.

4. Code Implementation: Provide a sample code snippet that illustrates the algorithm.

5. Complexity Analysis: Discuss the time and space complexity of your solution.

Key Points

• Definition of a Palindrome: A palindrome is a sequence that reads the same backward as forward.

• Dynamic Programming: This technique allows us to build solutions to larger problems using solutions to smaller subproblems.

• Optimization: Discuss how your approach minimizes redundant calculations.

• Real-World Applications: Understanding palindromic subsequences can be useful in DNA sequencing, data compression, and text analysis.

Standard Response

Here’s a structured response that encapsulates the approach and provides a clear answer:

Understanding the Problem
A palindromic subsequence is a sequence of characters in a string that can be read the same forwards and backwards. For example, in the string "character", the longest palindromic subsequence is "carac".

Choosing the Right Algorithm
The most efficient way to solve this problem is through dynamic programming. This method provides a systematic approach to build solutions for subproblems, ultimately leading to the solution for the full problem.

• Define the DP Table: Create a 2D array dp where dp[i][j] will hold the length of the longest palindromic subsequence in the substring from index i to j.

• Initialization: Each character is a palindrome of length 1. Thus, for all i, dp[i][i] = 1.

• Filling the DP Table: Loop through the string in reverse order. For each substring, check if the characters at the ends are the same:

• If they are the same, then dp[i][j] = dp[i+1][j-1] + 2.

• If not, take the maximum between dp[i+1][j] and dp[i][j-1].

• Extract the Result: The length of the longest palindromic subsequence will be found in dp[0][n-1], where n is the length of the string.

• Steps to Implement the Algorithm

def longest_palindromic_subsequence(s: str) -> int:
 n = len(s)
 dp = [[0] * n for _ in range(n)]
 
 # Every single character is a palindrome of length 1
 for i in range(n):
 dp[i][i] = 1

 # Build the table
 for length in range(2, n + 1): # length of the substring
 for i in range(n - length + 1):
 j = i + length - 1
 if s[i] == s[j]:
 dp[i][j] = dp[i + 1][j - 1] + 2
 else:
 dp[i][j] = max(dp[i + 1][j], dp[i][j - 1])

 return dp[0][n - 1]

Sample Code Implementation

• Time Complexity: O(n^2), where n is the length of the string. This is because we fill a 2D table of size n x n.

• Space Complexity: O(n^2) for the DP table.

• Complexity Analysis

Tips & Variations

Common Mistakes to Avoid

• Neglecting Edge Cases: Always consider empty strings or strings with one character.

• Incorrect DP Transitions: Ensure that the logic for filling the DP table is consistent and correctly implemented.

Alternative Ways to Answer

• Recursive Approach: Discuss a recursive solution with memoization as an alternative method, although less efficient than dynamic programming.

• Iterative Bottom-Up Approach: Explain how you could also implement this iteratively without recursion.

Role-Specific Variations

• Technical Roles: Focus on the implementation details, optimizations, and complexity analysis.

• Managerial Roles: Emphasize problem-solving and algorithmic thinking rather than technical details.

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