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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)

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Algorithm Design Techniques

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