12 Facts About Polynomial time

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

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

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

Specific term sublinear Polynomial 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 Polynomial time algorithms are typically randomized, and provide only approximate solutions.

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

Linear Polynomial time is the best possible Polynomial 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.

Quasi-polynomial time algorithms are algorithms that run longer than polynomial time, yet not so long as to be exponential time.

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

Exponential Polynomial 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 Polynomial time 2.

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