11 Facts About Computation time

In computer science, the time complexity is the computational complexity that describes the amount of computer time it takes to run an algorithm.

In both cases, the Computation time complexity is generally expressed as a function of the size of the input.

Algorithms taking logarithmic Computation time are commonly found in operations on binary trees or when using binary search.

Specific term sublinear Computation time algorithm is usually reserved to algorithms that are unlike the above in that they are run over classical serial machine models and are not allowed prior assumptions on the input.

Sub-linear Computation time algorithms are typically randomized, and provide only approximate solutions.

Sub-linear Computation time algorithms arise naturally in the investigation of property testing.

Linear Computation time is the best possible Computation time complexity in situations where the algorithm has to sequentially read its entire input.

Concept of polynomial time leads to several complexity classes in computational complexity theory.

In that case, this reduction does not prove that problem B is NP-hard; this reduction only shows that there is no polynomial Computation time algorithm for B unless there is a quasi-polynomial Computation time algorithm for 3SAT .

Exponential Computation time hypothesis is that 3SAT, the satisfiability problem of Boolean formulas in conjunctive normal form with, at most, three literals per clause and with n variables, cannot be solved in Computation time 2.

An example of an algorithm that runs in factorial Computation time is bogosort, a notoriously inefficient sorting algorithm based on trial and error.