🚀 Welcome to Part 72 of Code & Debug’s DSA in Python Course!
In this lecture, we extend our Advanced Recursion concepts by solving Count All Subsequences with Sum K using Recursion & Backtracking. This is a critical problem in Dynamic Programming, Combinatorics, and Optimization Problems.
📚 What you’ll learn in this video:
✅ Understanding the problem statement & constraints
✅ Recursive approach to count valid subsequences
✅ Optimizing using Backtracking to reduce redundant computations
✅ Time complexity analysis of the recursive approach
✅ Handling edge cases efficiently
💡 Why is this important?
This problem is fundamental for Subset Sum Problems, Combinatorial Counting, and Dynamic Programming. It is frequently asked in FAANG interviews and helps in efficiently solving counting-based problems using recursion and backtracking.
👉 Link to solve this question:
https://www.geeksforgeeks.org/problems/perfect-sum-problem5633/1
👉 Refer the article below for better understanding:
https://codeanddebug.in/blog/count-all-subsequences-with-sum-k/
👉 📄 Access the full YouTube DSA Playlist Sheet (All Questions in Order):
🔗 https://docs.google.com/spreadsheets/d/1AWE15Fy3wD2iqu2vjK_R7cCiuvSsjYQclcdZmHpF66o/edit?usp=sharing
👉 Enroll in the free DSA Python course here:
https://codeanddebug.in/course/master-dsa-with-leetcode
👉 Enroll for Self-Paced Advance DSA course here:
https://codeanddebug.in/course/zero-to-hero-python-dsa
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