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Everything about Analysis of algorithms

Study of resources used by an algorithm

In computer science, the analysis of algorithms is the process of finding the computational complexity of algorithms—the amount of time, storage, or other resources needed to execute them. Usually, this involves determining a function that relates the size of an algorithm's input to the number of steps it takes or the number of storage locations it uses. An algorithm is said to be efficient when this function's values are small, or grow slowly compared to a growth in the size of the input. Different inputs of the same size may cause the algorithm to have different behavior, so best, worst and average case descriptions might all be of practical interest. When not otherwise specified, the function describing the performance of an algorithm is usually an upper bound, determined from the worst case inputs to the algorithm. Wikipedia →

🧭 Start here: a 6-step reading path

  1. Read the overview: Analysis of algorithmsA 5-minute orientation
  2. Skim a recent paper: K-means clustering algorithms: A comprehensive review, variants analysis, and advances in the era of big dataInfluential work since 2022
  3. Pick up a classic book: Parallel Processing and Applied MathematicsThe most-reprinted book on the subject
  4. Read the landmark paper: Smoothed analysis of algorithmsCited 503 times
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🗂️ The subject at a glance

  1. Cost models
  2. Run-time analysis
  3. Relevance
  4. Constant factors

🏆 Landmark papers (most cited)

🆕 Influential since 2022

📚 Books worth reading

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