The ultimate guide to dynamic programming interviews. Developed by FAANG engineers, it equips you with strategic DP skills, practice with real-world questions, and patterns for efficient solutions.

Some of the toughest questions in technical interviews require dynamic programming solutions. Dynamic programming (DP) is an advanced optimization technique applied to recursive solutions. However, DP is not a one-size-fits-all technique, and it requires practice to develop the ability to identify the underlying DP patterns. With a strategic approach, coding interview prep for DP problems shouldn’t take more than a few weeks.

This course starts with an introduction to DP and thoroughly discusses five DP patterns. You’ll learn to apply each pattern to several related problems, with a visual representation of the working of the pattern, and learn to appreciate the advantages of DP solutions over naive solutions.

After completing this course, you will have the skills you need to unlock even the most challenging questions, grok the coding interview, and level up your career with confidence.

This course is also available in C++, JavaScript, and Python—with more coming soon!

Grokking Dynamic Programming Interview

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Help me debug only the function FindLpsLength.I don't need the corrected code.I don't want explanations or code reviews. Just provide me the exact and to-the-point error that helps me run the code successfully on the test cases provided.

Please help me understand why this code failed for the last failed test case. Please don't give me the corrected code. Just provide me exact and to-the-point advice on how to correct the code so that it runs successfully on the test cases provided. 

Your current task is to review FindLpsLength function And Answer the following questions: Ensure that the Palindromic Subsequence  pattern used? Does the code follow proper language guidelines. At the end, review the code.

Please provide a brief time complexity analysis of the function FindLpsLength.While calculating time complexity, include the time complexity of built-in Java functions invoked by the code. The Answer should be to the point and in a few lines.

Please provide a brief Space complexity analysis of the function FindLpsLength.While calculating space complexity, include the space complexity of built-in Java functions invoked by the code. The Answer should be to the point and in a few lines.

def evaluate_results(self) -> object:
        """
        self.__inputs is the list of inputs that are passed as input to function
        self.__actual_output is a list of outputs that is produced by the learner's code
        return : self.__edu__result object
        """
        # multiple outputs will be added at each index respectively for a single test_case
        expected_outputs = [] 
        # store the 1st expected output that should be displayed to the user 
        expected_outputs.append(self.__inputs[0] + self.__inputs[1])

        if json.dumps(self.__inputs[0] + self.__inputs[1]) == json.dumps(self.__actual_outputs[0]):
            self.__edu_result.store_result(expected_outputs, TestCaseResult.PASS)
        else:
            self.__edu_result.store_result(expected_outputs, TestCaseResult.FAIL)

        return self.__edu_result

import java.util.*;

class Main {
    public static int findLpsLength(String s){

        // Write your code here
        
        return -1;
    }
}

testcases:
  - name: "testcase 1"
    inputs:
    - 1 : "a"
    output: 
    - 1 : 1
  - name: "testcase 2"
    inputs:
    - 1: "bbbbbbbbbbbbbbbbbbbb"
    output:
    - 1 : 20
  - name: "testcase 3"
    inputs:
    - 1: "ghikjlmnopqrstuvwxyz"
    output:
    - 1 : 1
  - name: "testcase 4"
    inputs:
    - 1: "abcdefgfedcba"
    output:
    - 1 : 13
  - name: "testcase 5"
    inputs:
    - 1: "mnopqrsttsrqponm"
    output:
    - 1 : 16
  - name: "testcase 6"
    inputs:
    inputs:
    - 1: "mwusjunybvgafxuhloqwfoizqkkqzilltjw"
    output:
    - 1 : 8  
  

testcases:
  - name: "testcase 1"
    inputs:
    - 1 : "abcbq"
    output: 
    - 1 : 3
  - name: "testcase 2"
    inputs:
    - 1: "dad"
    output:
    - 1 : 3
  - name: "testcase 3"
    inputs:
    - 1: "afternoon"
    output:
    - 1 : 4
  - name: "testcase 4"
    inputs:
    - 1: "xyz"
    output:
    - 1 : 1    

def sum(input_1, input_2):
    return input_1+input_2

def validate_inputs(self) -> None:
        """
        self.__inputs is the list of inputs that are passed as input to function
        store the result using the self.store_result(error_msg, status)
        return : void
        """
        # inputs can be accessed using self.__inputs starting from 0 to len(self.__inputs) - 1
        if self.__inputs[0] > 1000:
            self.store_result ("input 1 should be less than 1000", ValidationFunction.FAIL)
        elif self.__inputs[1] > 1000:
            self.store_result("input 2 should be less than 1000", ValidationFunction.FAIL)
        else:
            self.store_result("", ValidationFunction.PASS)


import java.util.stream.*;
import java.util.*;

class LongestPalindromicSubstring {
    public static int findLpsLengthRecursion(String s, int start, int end){
        // if both pointers are pointing to the same index
        if (start==end)
            return 1;

        // the characters at start and end indices match
        if (s.charAt(start)==s.charAt(end)){
            int subStringLength = end - start + 1;

            // if the substring length is 2 and it's also a palindrome
            if (subStringLength == 2)
                return 2;

            // check whether the remainig string is a palindrome
            if (subStringLength == 2 + findLpsLengthRecursion(s, start+1, end-1))
                return subStringLength;
        }
        // skip one element either from the beginning or end and select the maximum resultant value
        return Math.max(findLpsLengthRecursion(s, start+1, end), findLpsLengthRecursion(s, start, end-1));
    }

    public static int findLpsLength(String s){
        return findLpsLengthRecursion(s, 0, s.length()-1);
    }

    // Driver code
    public static void main(String[] args) {
        ArrayList<String> strings = new ArrayList<String>(
            Arrays.asList("abcbda", "tworacecars", "pqrssrqp", "mnop", "bbbbbbbbbbbbbbbbbbbb"));
        
        // You can uncomment the line below and check how this recursive solution causes a time-out
        // strings.add("mwusjunybvgafxuhloqwfoizqkkqzilltjw");

        for (int i=0; i<strings.size(); ++i){
            System.out.println(i+1+".\t Input string: '" + strings.get(i) + "'");
            System.out.println("\t Length of the longest palindromic substring: " + findLpsLength(strings.get(i)));
            System.out.println(new String(new char[100]).replace('\0', '-'));
        }
    }
}

Java

Let's solve the Longest Palindromic Substring problem using Dynamic Programming.

Getting Started

0/1 Knapsack

Unbounded Knapsack

Recursive Numbers

Longest Common Substring

Palindromic Subsequence

Conclusion

Longest Palindromic Substring