Dynamic Programming (DP) Algorithm
Dynamic programming is a algorithmic technique that solves complex problems by breaking them down into smaller subproblems and storing their solutions for future use. It is particularly effective for problems that contains overlapping subproblems and optimal substructure.
Common Algorithms that Use Dynamic Programming:
- Longest Common Subsequence (LCS): Finds the longest common subsequence between two strings.
- Shortest Path in a Graph: Finds the shortest path between two nodes in a graph.
- Knapsack Problem: Determines the maximum value of items that can be placed in a knapsack with a given capacity.
- Matrix Chain Multiplication: Optimizes the order of matrix multiplication to minimize the number of operations.
- Fibonacci Sequence: Calculates the nth Fibonacci number.
Dynamic Programming or DP
Dynamic Programming is a method used in mathematics and computer science to solve complex problems by breaking them down into simpler subproblems. By solving each subproblem only once and storing the results, it avoids redundant computations, leading to more efficient solutions for a wide range of problems. This article provides a detailed exploration of dynamic programming concepts, illustrated with examples.
Table of Content
- What is Dynamic Programming ?
- How Does Dynamic Programming Work?
- Examples of Dynamic Programming
- When to Use Dynamic Programming?
- Approaches of Dynamic Programming
- Dynamic Programming Algorithm
- Advantages of Dynamic Programming
- Applications of Dynamic Programming
- Learn Basic of Dynamic Programming
- Advanced Concepts in Dynamic Programming
- Dynamic Programming Problems