Algorithm Analysis Techniques

 To evaluate the efficiency of an algorithm, we analyze its performance using the following measures:

1. Time Complexity

• Represents the time taken by an algorithm to run as a function of input size (n).

• Expressed using Big-O notation (O).

• Examples:

  • O(1) - Constant time

  • O(log n) - Logarithmic time (Binary Search)

  • O(n) - Linear time (Linear Search)

  • O(n log n) - Log-linear time (Merge Sort)

  • O(n²) - Quadratic time (Bubble Sort)

2. Space Complexity

• Represents the memory required by an algorithm.

• Important for optimizing performance in memory-constrained environments.

3. Best, Average, and Worst Case Analysis

• Best Case: Minimum time required (ideal scenario)

• Average Case: Expected performance over different inputs

• Worst Case: Maximum time required (upper bound)