Recursion, Backtracking In Programming
Published 2/2025
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 226.05 MB | Duration: 0h 55m
Learn Recursion, Backtracking in Programming and be prepared for interviews!
What you'll learn
Understanding Recursion techniques
Understand the data structures
Implement backtracking methods
Improve problems solving skills
Requirements
Must have any basics knowledge in any programming language
Description
You are going to learn Recursion, Backtracking in Programming. Recursion and backtracking are two closely related concepts in programming that play a crucial role in solving complex problems efficiently. They are widely used in algorithms involving search, enumeration, and optimization. Understanding these concepts is essential for mastering problem-solving techniques in data structures and algorithms.Recursion is a problem-solving technique in which a function calls itself to break down a problem into smaller subproblems of the same type. This approach is useful for tasks that can be divided into identical but smaller tasks, such as computing factorials, traversing trees, and solving mathematical series. A recursive function typically consists of two key components: a base case, which terminates the recursion, and a recursive case, which reduces the problem size by calling the function itself with modified parameters.Backtracking is an advanced form of recursion used for systematic searching through possible solutions. It is a depth-first approach where a partial solution is built incrementally, and whenever a constraint is violated, the algorithm discards the current path and backtracks to the previous step. This method is particularly useful for combinatorial problems, such as the N-Queens problem, Sudoku solving, and generating permutations.Recursion and backtracking are powerful techniques that simplify complex problems. While recursion helps break problems into smaller parts, backtracking enables efficient searching by eliminating unfeasible solutions. Together, they form the backbone of many algorithmic solutions in computing.
Overview
Section 1: Introduction
Lecture 1 Introduction
Lecture 2 Backtracking blueprint
Section 2: Method and Complexity Analysis
Lecture 3 Method and Complexity Analysis
Lecture 4 Recursion methods
Section 3: Recursion Tree
Lecture 5 Recursion Tree
Lecture 6 Complexity Analysis in Backtracking
Section 4: Runcode Compalization
Lecture 7 Runcode Compalization
It is for beginners who are not aware of data structures and algorithmic topics
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