Searching Algorithms in C++ & Python 🔍⚡

This repository contains C++ and Python implementations of various searching algorithms. Each algorithm is well-documented, optimized for general use, and includes time complexity analysis. Ideal for learning, interviews, and competitive programming! 🚀

📌 Included Searching Algorithms

1️⃣ Linear Searching

🔹 Linear Search – Iterates through the array to find an element (O(n)).

2️⃣ Binary Searching

🔹 Binary Search – Efficient search for sorted arrays using divide & conquer (O(log n)).
🔹 Ternary Search – Similar to binary search but splits into three parts (O(log n)).

3️⃣ Interpolation Searching

🔹 Interpolation Search – Works well on uniformly distributed data (O(log log n) avg).

4️⃣ Jump Searching

🔹 Jump Search – Faster than linear search; jumps ahead by fixed steps (O(√n)).
🔹 Exponential Search – Starts with small steps, then switches to binary search (O(log n)).

5️⃣ Hashing-Based Search

🔹 Hash Table Search – Uses hash functions for constant-time lookups (O(1) avg).

📂 Folder Structure

📂 Searching-Algorithms
│── 📜 linear_search.cpp
│── 📜 binary_search.cpp
│── 📜 ternary_search.cpp
│── 📜 interpolation_search.cpp
│── 📜 jump_search.cpp
│── 📜 exponential_search.cpp
│── 📜 hash_search.cpp
│── 📜 linear_search.py
│── 📜 binary_search.py
│── 📜 ternary_search.py
│── 📜 interpolation_search.py
│── 📜 jump_search.py
│── 📜 exponential_search.py
│── 📜 hash_search.py
│── 📜 README.md  

⏳ Time Complexity Comparison

Algorithm	Best Case	Average Case	Worst Case
Linear Search	O(1)	O(n)	O(n)
Binary Search	O(1)	O(log n)	O(log n)
Ternary Search	O(1)	O(log n)	O(log n)
Interpolation Search	O(1)	O(log log n)	O(n)
Jump Search	O(1)	O(√n)	O(√n)
Exponential Search	O(1)	O(log n)	O(log n)
Hash Table Search	O(1)	O(1)	O(n)

💡 How to Use?

1️⃣ Clone this repository

git clone https://github.com/CyberMatic-AmAn/Searching.git

2️⃣ Compile and run any searching program
For C++:

g++ binary_search.cpp -o binary_search
./binary_search

For Python:

python binary_search.py

📌 Contributions

Feel free to fork this repo, improve the code, and submit pull requests! 🚀