Are you an EPFL student looking for a semester project?
Work with us on data science and visualisation projects, and deploy your project as an app on top of Graph Search.
Concept
Computational complexity of mathematical operations
Summary
The following tables list the computational complexity of various algorithms for common mathematical operations.
Here, complexity refers to the time complexity of performing computations on a multitape Turing machine. See big O notation for an explanation of the notation used.
Note: Due to the variety of multiplication algorithms, below stands in for the complexity of the chosen multiplication algorithm.
This table lists the complexity of mathematical operations on integers.
Many of the methods in this section are given in Borwein & Borwein.
The elementary functions are constructed by composing arithmetic operations, the exponential function (), the natural logarithm (), trigonometric functions (), and their inverses. The complexity of an elementary function is equivalent to that of its inverse, since all elementary functions are analytic and hence invertible by means of Newton's method. In particular, if either or in the complex domain can be computed with some complexity, then that complexity is attainable for all other elementary functions.
Below, the size refers to the number of digits of precision at which the function is to be evaluated.
It is not known whether is the optimal complexity for elementary functions. The best known lower bound is the trivial bound
This table gives the complexity of computing approximations to the given constants to correct digits.
Algorithms for number theoretical calculations are studied in computational number theory.
Computational complexity of matrix multiplication
The following complexity figures assume that arithmetic with individual elements has complexity O(1), as is the case with fixed-precision floating-point arithmetic or operations on a finite field.
In 2005, Henry Cohn, Robert Kleinberg, Balázs Szegedy, and Chris Umans showed that either of two different conjectures would imply that the exponent of matrix multiplication is 2.
Algorithms for computing transforms of functions (particularly integral transforms) are widely used in all areas of mathematics, particularly analysis and signal processing.
About this result
This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.