Divide and Conquer
- Fact: Divide and Conquer is a problem-solving paradigm where a problem is broken into subproblems that are solved independently.
- Example:
- Merge Sort in Python: An algorithm that divides the unsorted list into n sublists, each containing one element, and then repeatedly merges sublists to produce new sorted sublists.
Python3
def merge_sort(arr): if len (arr) > 1 : mid = len (arr) / / 2 left_half = arr[:mid] right_half = arr[mid:] merge_sort(left_half) merge_sort(right_half) merge(arr, left_half, right_half) def merge(arr, left, right): i = j = k = 0 while i < len (left) and j < len (right): if left[i] < right[j]: arr[k] = left[i] i + = 1 else : arr[k] = right[j] j + = 1 k + = 1 while i < len (left): arr[k] = left[i] i + = 1 k + = 1 while j < len (right): arr[k] = right[j] j + = 1 k + = 1 |
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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