The ultimate dynamic programming guide by FAANG engineers. Structured prep with real-world DP questions to get interview-ready in hours!

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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 JavaScript, C++, and Java—with more coming soon!

Grokking Dynamic Programming Interview in Python

# Statement
Given a set of positive numbers `arr` and a value `total`, determine if there exists a subset in the given set whose sum is equal to `total`. A subset can be an empty set, or it can either consist of some elements of the set or all the elements of the set.

Let's say you are given a set = {1, 2, 3, 7} and a total = 6. The output will be TRUE as the subset = {1, 2, 3} adds up to make the desired total (1+2+3) = 6.

**Constraints:**

- $1\leq$  `arr.length` $\leq 900$
- $0\leq$ `total`  $\leq 10^5$
- $1\leq$  `arr[i]` $\leq 10^4$

# Examples



Let's solve the Subset Sum problem using Dynamic Programming.

Getting Started

0/1 Knapsack

Unbounded Knapsack

Recursive Numbers

Longest Common Substring

Palindromic Subsequence

Conclusion

Subset Sum