✅Arithmetic Slices II Subsequence - LeetCode 446 - Dynamic Programming - Arrays - Explained

In this video, we delve into a dynamic programming solution for counting arithmetic subsequences in an integer array. This problem requires a strategic approach to efficiently find and count sequences that exhibit a common difference.

Intuition:
The trick lies in sorting jobs by their end times and using dynamic programming to incrementally build the maximum profit while considering non-overlapping jobs. The solution skillfully balances between including a new job for maximum profit and sticking with the previously accumulated profit.

Key Steps:

Sorting Jobs: Sort jobs based on their end times for sequential processing.
Dynamic Programming Table (dp): Initialize a DP table with zeros to store maximum profit up to each job.
Binary Search: Utilize binary search to find the closest job that finished before the current job's start.
Profit Calculation: Determine the maximum profit, including or excluding the current job, by balancing profit values.
Result: The last entry in the DP array contains the maximum achievable profit.
🎓 Dive deep into the code implementation and grasp the intricacies of dynamic programming for optimal job scheduling.

#leetcode #leetcodechallenge #leetcodedailychallenge #potd
#DynamicProgramming #Algorithm #CodingExplained #ProgrammingTutorial #ArithmeticSubsequences