Dynamic Programming
- Fact: Dynamic programming is an optimization technique used to solve problems by breaking them down into overlapping subproblems.
- Example:
- Fibonacci Sequence with Dynamic Programming: A more efficient implementation of the Fibonacci sequence using dynamic programming.
Python3
def fibonacci(n): fib = [ 0 ] * (n + 1 ) fib[ 1 ] = 1 for i in range ( 2 , n + 1 ): fib[i] = fib[i - 1 ] + fib[i - 2 ] return fib[n] |
Output
25 Interesting DSA Terms/Concepts Every Programmer Should Know
Data Structures and Algorithms (DSA) form the backbone of computer science, playing a pivotal role in efficient problem-solving and software development. Here are 25 interesting Data Structures and Algorithms (DSA) terms/concepts that every programmer should know. Understanding these concepts is crucial for developing efficient algorithms and solving a variety of programming challenges.
Table of Content
- Definition of Data Structure
- Types of Data Structures
- Importance of Algorithms
- Dynamic Arrays
- Linked Lists
- Time Complexity
- Space Complexity
- Tree Structures
- Hash Tables
- Graphs
- Searching Algorithms
- Sorting Algorithms
- Dynamic Programming
- Divide and Conquer
- NP-Completeness
- Heaps
- Trie Data Structure
- B-Trees
- AVL Trees
- In-Place Algorithms
- Bellman-Ford Algorithm
- Floyd-Warshall Algorithm
- Dijkstra’s Algorithm
- Big-O Notation
- NP-Hard Problems
Let’s delve into 25 intriguing concepts of DSA, each accompanied by detailed examples for a comprehensive understanding.
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