Design & Analysis of Algorithms - Vol 1 | 6. Input Size and Running Time by Abraham | Learn Smarter
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6. Input Size and Running Time

Efficiency in algorithm performance is evaluated based on input size and basic operations to compute running time functions. Worst-case analysis provides an upper bound on resource consumption while understanding input characteristics is crucial for algorithm design. Average case analysis, although appealing, presents challenges in estimation, rendering worst-case analysis a practical focus in algorithm evaluation.

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Sections

  • 6.1

    Input Size And Running Time

    This section discusses the relationship between input size and the running time of algorithms, focusing on how input size can vary depending on the problem context.

    Learning Practice

  • 6.1.1

    Definition Of Input Size

    This section explores the significance of input size in algorithm efficiency, emphasizing its impact on performance metrics like running time.

    Learning Practice

  • 6.1.2

    Examples Of Input Sizes

    This section discusses the importance of input size in measuring algorithm efficiency, addressing worst-case scenarios and how they affect performance analysis.

    Learning Practice

  • 6.1.3

    Special Case: Input Size For Numbers

    This section discusses the concept of input size when analyzing algorithms, specifically focusing on how the size of numbers can be measured in terms of their digits rather than their magnitude.

    Learning Practice

  • 6.1.4

    Ignoring Constants In Analysis

    This section discusses the importance of input size in algorithm analysis and the concept of ignoring constants to evaluate the efficiency of algorithms.

    Learning Practice

  • 6.2

    Worst Case And Average Case Analysis

    This section explores the concepts of worst case and average case analysis in algorithm efficiency, highlighting the significance of input size and its impact on running time.

    Learning Practice

  • 6.2.1

    Definition Of Worst Case

    This section discusses the concept of the worst-case scenario in algorithm analysis, emphasizing its importance in measuring an algorithm's efficiency based on input size and behavior.

    Learning Practice

  • 6.2.2

    Understanding Average Case Analysis

    This section discusses how the efficiency of an algorithm can be measured through average case analysis, highlighting key concepts such as input size and worst case scenario.

    Learning Practice

  • 6.2.3

    Summary Of Worst Case Vs. Average Case

    This section explores the concepts of worst-case and average-case scenarios in algorithm analysis, emphasizing their significance in assessing algorithm efficiency.

    Learning Practice

References

ch6.pdf

Class Notes

Memorization

What we have learnt

  • The running time of an algo...
  • Worst-case the analysis is ...
  • Input size can vary dependi...

Final Test

Revision Tests