Dynamic Programming Algorithms Every Programmer Should Know

Dynamic programming is a popular technique in computer science and software engineering that allows for solving complex problems by breaking them down into smaller subproblems. This post explores various dynamic programming algorithms every programmer should know, including Fibonacci numbers, longest common subsequence, knapsack problem, edit distance, maximum subarray, coin change, matrix chain multiplication, longest increasing subsequence, traveling salesman problem, 0-1 integer programming, egg dropping problem, counting bits, perfect squares, partition equal subset sum, unique paths, unique paths II, and more.

#algorithms

#dynamic-programming

#general-programming

Apr 21, 2023•16m read time•From dev.to

Table of contents

Fibonacci Numbers Longest Common Subsequence Knapsack Problem Edit Distance Maximum Subarray Coin Change Edit Distance with Allowed Operations Longest Palindromic Substring Maximum Product Subarray Largest Rectangle in a Histogram Egg Dropping Problem Counting Bits Perfect Squares Partition Equal Subset Sum Longest Common Substring Unique Paths Edit Distance with Allowed Operations Subset Sum Problem Longest Palindromic Substring Longest Palindromic Subsequence Maximum Product Subarray Largest Rectangle in a Histogram Egg Dropping Problem Counting Bits Perfect Squares Partition Equal Subset Sum Unique Paths Unique Paths II Conclusion