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Everything about Time complexity

Estimate of time taken for running an algorithm

In theoretical computer science, the time complexity is the computational complexity that describes the amount of computer time it takes to run an algorithm. Time complexity is commonly estimated by counting the number of elementary operations performed by the algorithm, supposing that each elementary operation takes a fixed amount of time to perform. Thus, the amount of time taken and the number of elementary operations performed by the algorithm are taken to be related by a constant factor. Wikipedia →

🧭 Start here: a 6-step reading path

  1. Read the overview: Time complexityA 5-minute orientation
  2. Skim a recent paper: Time Complexity in Deep Learning ModelsInfluential work since 2022
  3. Pick up a classic book: PreyThe most-reprinted book on the subject
  4. Read the landmark paper: MUSCLE: a multiple sequence alignment method with reduced time and space complexityCited 8,061 times
  5. Go sideways: Space complexityA closely related topic
  6. Go sideways: Computational complexityA closely related topic

📖 Definitions

Noun The amount of time an algorithm requires to run, as a function of the amount of input, measured in such a way as to ignore constant terms and multiplication by constant terms.

🗂️ The subject at a glance

  1. Table of common time complexities
  2. Logarithmic time
  3. Polylogarithmic time
  4. Sub-linear time
  5. Linear time
  6. Quasilinear time
  7. Sub-quadratic time
  8. Polynomial time
  9. Superpolynomial time
  10. Quasi-polynomial time
  11. Sub-exponential time
  12. Exponential time

🏆 Landmark papers (most cited)

🆕 Influential since 2022

📚 Books worth reading

Ranked by number of editions on Open Library, a rough measure of how lasting a book is.

🔗 Related topics