| CARVIEW |
Intermediate
25h
Updated 1 month ago
Grokking Dynamic Programming Interview in JavaScript
WHAT YOU'LL LEARN
Show more
Content
1.
Getting Started
3 Lessons
2.
0/1 Knapsack
9 Lessons
3.
Unbounded Knapsack
6 Lessons
4.
Recursive Numbers
12 Lessons
5.
Longest Common Substring
16 Lessons
6.
Palindromic Subsequence
6 Lessons
7.
Conclusion
1 Lessons
Trusted by 2.8 million developers working at companies
Anthony Walker
@_webarchitect_
Evan Dunbar
ML Engineer
Software Developer
Carlos Matias La Borde
Souvik Kundu
Front-end Developer
Vinay Krishnaiah
Software Developer
See how Educative uses AI to make your learning more immersive than ever before.
AI Prompt
Code Feedback
Explain with AI
AI Code Mentor
Related Courses and Skill Paths
Free Resources
Frequently Asked Questions
How can I improve my intuition for solving dynamic programming problems?
To develop a strong intuition for DP problems, start by solving simpler problems like Fibonacci or coin change, focusing on how subproblems overlap. Practice breaking down problems into smaller components and recognize patterns like overlapping subproblems and optimal substructure. Gradually move on to more complex problems and study different DP patterns to build understanding.
Why is dynamic programming considered more efficient than brute-force solutions?
Dynamic programming is more efficient because it eliminates redundant computations by storing the results of subproblems. In brute-force methods, the same subproblems are often recalculated multiple times, leading to exponential time complexity. DP reduces this by solving each subproblem once and using that result whenever needed, cutting the overall time complexity.
How do I choose between memoization and tabulation in a dynamic programming problem?
The choice between memoization and tabulation depends on the problem and the desired approach. If you’re more comfortable with recursion and want to solve the problem top-down, memoization is ideal. Tabulation, however, is preferred when you can iteratively build the solution from base cases up in a bottom-up manner, and it’s often easier to visualize and manage certain problems.
What are some common mistakes to avoid when solving dynamic programming problems?
One common mistake is not correctly identifying overlapping subproblems or optimal substructure, leading to inefficient solutions. Another issue is poor table or memoization setup, such as failing to initialize base cases correctly. Also, forgetting to handle edge cases or ignoring time and space complexity optimizations can lead to suboptimal performance.
How can dynamic programming be applied in real-world applications?
Dynamic programming is used in various real-world applications, such as optimization problems in finance (e.g., portfolio optimization), operations research (e.g., supply chain management), and computer science (e.g., text processing, network routing). It’s particularly useful in problems where decisions must be made sequentially, and resource optimization is critical.
