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Algorithms

Introduction

An algorithm is a finite sequence of well-defined, computer-implementable instructions, typically to solve a class of problems or to perform a computation. It's the 'recipe' that tells a computer exactly what steps to take to get from an input to a desired output. Studying algorithms is not just about memorizing steps; it's about learning different problem-solving strategies and understanding their efficiency. A well-designed algorithm can save significant time and computational resources.

Core Concepts

Core Concepts

1. Algorithm Analysis & Big O Notation

  • Time Complexity: How the runtime of an algorithm grows as the input size grows.
  • Space Complexity: How the memory usage of an algorithm grows as the input size grows.
  • Big O Notation: A mathematical notation used to describe the limiting behavior of a function when the argument tends towards a particular value or infinity. It describes the worst-case scenario.
    • O(1): Constant time (e.g., accessing an array element by index).
    • O(log n): Logarithmic time (e.g., binary search).
    • O(n): Linear time (e.g., searching an unsorted list).
    • O(n log n): Log-linear time (e.g., efficient sorting algorithms).
    • O(n²): Quadratic time (e.g., simple sorting algorithms like bubble sort).

2. Sorting Algorithms Putting elements of a list in a certain order.

  • Bubble Sort, Selection Sort, Insertion Sort: Simple but inefficient (O(n²)). Good for learning.
  • Merge Sort, Quicksort, Heapsort: Efficient, divide-and-conquer algorithms (O(n log n)).

3. Searching Algorithms Finding a specific item in a collection.

  • Linear Search: Scans the list sequentially. Inefficient (O(n)).
  • Binary Search: Efficiently finds an item in a sorted list by repeatedly dividing the search interval in half. Very fast (O(log n)).

4. Graph Algorithms Algorithms that operate on graph data structures.

  • Traversal: Visiting all the nodes in a graph.
    • Breadth-First Search (BFS): Explores neighbor nodes first (level by level).
    • Depth-First Search (DFS): Explores as far as possible along each branch before backtracking.
  • Shortest Path Algorithms: Finding the shortest path between two nodes (e.g., Dijkstra's Algorithm, Bellman-Ford).
  • Minimum Spanning Tree (MST): Finding a subset of edges that connects all vertices with the minimum total edge weight (e.g., Prim's, Kruskal's).

5. Algorithmic Paradigms General approaches to constructing efficient solutions.

  • Brute Force: Trying every possible solution. Simple, but often too slow.
  • Greedy Algorithms: Making the locally optimal choice at each stage with the hope of finding a global optimum.
  • Divide and Conquer: Breaking a problem into smaller subproblems, solving them recursively, and combining their solutions. (e.g., Merge Sort).
  • Dynamic Programming: Breaking a problem down into simpler subproblems and storing the solution to each subproblem so it's only solved once. (e.g., finding Fibonacci numbers).

Examples & Use Cases

Examples & Use Cases

Binary Search (Python)

def binary_search(sorted_list, target): left, right = 0, len(sorted_list) - 1 while left <= right: mid = (left + right) // 2 if sorted_list[mid] == target: return mid # Found the target elif sorted_list[mid] < target: left = mid + 1 else: right = mid - 1 return -1 # Target not in list

Dynamic Programming - Fibonacci (JavaScript)

// Using memoization to avoid re-computing values const fibCache = {}; function fib(n) { if (n in fibCache) { return fibCache[n]; } if (n <= 1) { return n; } const result = fib(n - 1) + fib(n - 2); fibCache[n] = result; return result; }

Practice Questions

Practice Questions

1. MCQ: What is the worst-case time complexity of Binary Search? A) O(n) B) O(n²) C) O(log n) D) O(1)

2. Short Answer: Explain the core idea behind the "Divide and Conquer" paradigm. Give an example of an algorithm that uses it.

3. Coding Task: Implement the Bubble Sort algorithm to sort a list of numbers in ascending order.

Quick Summary

Quick Summary

  • An algorithm is a step-by-step procedure for solving a problem.
  • Big O Notation is used to analyze an algorithm's efficiency in terms of time and space.
  • Searching and Sorting are fundamental algorithmic problems with various solutions of differing efficiency.
  • Graph algorithms (like BFS, DFS, Dijkstra's) are crucial for solving network-related problems.
  • Algorithmic paradigms (Greedy, Divide and Conquer, Dynamic Programming) are high-level strategies for designing algorithms.

Further Reading

Introduction to Algorithms (CLRS) by Cormen, Leiserson, Rivest, and Stein

(Book (The definitive reference))

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LeetCode

(Coding Practice Platform)

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Khan Academy: Algorithms

(Free Online Course)

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