Dynamic Programming - The Algorists

Dynamic Programming:
0/1 Knapsack Concept

DP Fundamentals 0/1 Knapsack Core Concept Subset Sum Problem
Partition Equal Subset Sum Count Subset Sum Minimum Subset Sum Difference Target Sum Combination Sum Combination Sum with
Additional Constraint

Dynamic Programming:
Unbounded Knapsack Concept

Core Concept Coin Change:
Minimum no. of Coins Coin Change:
Total no. of Combinations Possible Combination Sum Combination Sum Count

Dynamic Programming:
State Machine Approach

Core Concept Stock Trading With Cooldown Max Profit w/ Unlimited
Stock Trade Transactions Stock Trading With Transaction Fee Max Profit w/ at most
2 Stock Trade Transactions Max Profit w/ at most
K Stock Trade Transactions Max Profit w/ at most
1 Stock Trade Transaction

Dynamic Programming:
Longest Increasing Subsequence

Core Concept Box Stacking Russian Doll Envelopes Largest Divisible Subset Longest String Chain Best Team with No Conflict Longest Bitonic Subsequence

Dynamic Programming:
All possible Cuts
in all possible Intervals
for the Last Operation

Core Concept Matrix Chain Multiplication
or, Parenthesization Optimal Binary Search Tree Minimum Palindrome Partitioning Burst Balloons

Dynamic Programming:
Kadane's Algorithm

Core Concept Maximum Product Subarray Best Time to Buy and Sell Stock

Dynamic Programming:
2-Strings Problems

Core Concept Longest Common Substring Longest Common Subsequence Shortest Common Supersequence Interleaving String Edit Distance

Dynamic Programming:
1-String Problem

Core Concept Palindrome Longest Palindromic Substring Longest Palindromic Subsequence Minimum Palindrome Partitioning Minimum Palindromic Insertion

Dynamic Programming:
Advanced String Problems

Regular Expression Matching Wildcard Matching

Dynamic Programming:
Other Techniques

Optimal Path(s) to Target Minimum Cost Tickets Counting DP Unique Paths Unique Paths Without Obstructions Decision Making Approach House Robber