What is Algorithm?

In previous section, we learned about Data Structures. Now, we will learn about Algorithms.

🧠 Algorithm Definition

An algorithm is:

• A step-by-step instruction to solve a specific problem
• But these steps must be finite and clear
• It takes some input, processes it, and produces an output

🌍 Real World Example

If we want to make a cup of tea, the algorithm would be:

1. Boil water
2. Add tea leaves to a cup
3. Pour boiling water into the cup
4. Let it steep for a few minutes
5. Add sugar or milk if desired
6. Stir and enjoy your tea! This is a simple algorithm for making tea. It has clear steps and produces a cup of tea as output.

🗂️ Types of Algorithms

There are many types of algorithms, but here are some common ones:

Algorithms are step-by-step procedures or formulas for solving problems. They come in many types, each suited for different tasks. Here’s a structured overview of the main types of algorithms:

🔁 1. Brute Force Algorithms

• Try all possible solutions until the correct one is found.
• Simple but inefficient for large inputs.
• Example: Trying every password combination.

🧠 2. Greedy Algorithms

• Make the best choice at each step, hoping for a global optimum.
• Fast and efficient, but not always accurate.
• Example: Dijkstra’s algorithm for shortest path.

🔄 3. Divide and Conquer Algorithms

• Break the problem into smaller sub-problems, solve them recursively, and combine results.
• Efficient for large problems.
• Example: Merge Sort, Quick Sort.

🔁 4. Dynamic Programming

• Break problems into overlapping sub-problems and store results to avoid recomputation.
• Great for optimization problems.
• Example: Fibonacci sequence, Knapsack problem.

🔍 5. Backtracking Algorithms

• Try solutions recursively and backtrack when a solution fails.
• Used in constraint satisfaction problems.
• Example: Sudoku solver, N-Queens problem.

🔗 6. Recursive Algorithms

• Solve a problem by solving smaller instances of the same problem.
• Elegant but can be memory-intensive.
• Example: Factorial, Tree traversals.

📊 7. Sorting Algorithms

• Arrange data in a specific order.
• Examples:
  • Bubble Sort
  • Merge Sort
  • Quick Sort
  • Heap Sort

🔎 8. Searching Algorithms

• Find elements in data structures.
• Examples:
  • Linear Search
  • Binary Search
  • Depth-First Search (DFS)
  • Breadth-First Search (BFS)

🧮 9. Hashing Algorithms

• Map data to fixed-size values for fast lookup.
• Example: Hash tables, SHA algorithms.

🧩 Algorithm Characteristics

An algorithm should have the following characteristics:

1. Input: Algorithms take zero or more inputs.
2. Output: Algorithms produce one or more outputs.
3. Finiteness: Algorithms must terminate after a finite number of steps.
4. Effectiveness: Each step of an algorithm must be clear and unambiguous.
5. Generality: Algorithms should solve a class of problems, not just a single instance.
6. Efficiency: Algorithms should use resources (time and space) optimally.

✅ Conclusion

In this section, we explored:

• Algorithm Definition: Step-by-step instructions to solve specific problems
• Algorithm Types: Brute force, greedy, divide and conquer, dynamic programming, backtracking, recursive, sorting, searching, and hashing algorithms
• Key Characteristics: Input, output, finiteness, effectiveness, generality, and efficiency

Understanding these fundamental concepts is crucial for developing efficient and effective solutions to complex problems in computer science and programming.