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Mastering Dynamic Programming II
Manual tabulation and workout is a great way to start grokking, analyzing, and spotting patterns, as well as strengthening our understanding and intuitions
Hello and welcome back to Mastering Dynamic Programming.
In the earlier post on Mastering Dynamic Programming, we went through the fundamentals of dynamic programming, its characteristics, and key attributes.
As dynamic programming is often one of the types of questions appearing in technical coding assessments conducted for the data scientist, machine learning engineer, or software engineer roles, we talked about how to identify and know when to apply dynamic programming.
We also explored the differences between the top-down and bottom-up approaches, and following that, we learned how to solve dynamic programming problems by running through three examples that include detailed explanations and solutions.
If you have missed the earlier post on Mastering Dynamic Programming, visit the link below to learn more.
In this post, we will further brush up on our skills to solve dynamic programming problems and showcase how to look out for possible ways to improve a solution.
We will dive deep into the details of one particular dynamic programming problem, Best Time to Buy and Sell Stock IV. The difficulty level for this problem is indicated as “Hard” on LeetCode.
Important Disclaimer: This is not a post on stock investment or trading, and it doesn’t contain any financial, investment, or trading advice or strategy. The example used and illustrated in this post is solely for the demonstration of dynamic programming.
Table of Contents
1. Problem Statement
2. Profit Table
∘ Profits with 1 Transaction
∘ Profits with 2 Transactions
3. Find Patterns
∘ Scenario 1 — Not Making a Profit on The Day
∘ Scenario 2 — Making a Profit…
