Computational complexity of mathematical operations

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Graphs of functions commonly used in the analysis of algorithms, showing the number of operations versus input size for each function

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.[1] 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.

Arithmetic functions[edit]

Operation Input Output Algorithm Complexity
Addition Two -digit numbers, and One -digit number Schoolbook addition with carry
Subtraction Two -digit numbers, and One -digit number Schoolbook subtraction with borrow
Multiplication Two -digit numbers
One -digit number Schoolbook long multiplication
Karatsuba algorithm
3-way Toom–Cook multiplication
-way Toom–Cook multiplication
Mixed-level Toom–Cook (Knuth 4.3.3-T)[2]
Schönhage–Strassen algorithm
Fürer's algorithm[3]
Harvey-Hoeven algorithm[4][5]
Division Two -digit numbers One -digit number Schoolbook long division
Burnikel–Ziegler Divide-and-Conquer Division[6]
Newton–Raphson division
Square root One -digit number One -digit number Newton's method
Modular exponentiation Two -digit integers and a -bit exponent One -digit integer Repeated multiplication and reduction
Exponentiation by squaring
Exponentiation with Montgomery reduction

Algebraic functions[edit]

Operation Input Output Algorithm Complexity
Polynomial evaluation One polynomial of degree with fixed-size coefficients One fixed-size number Direct evaluation
Horner's method
Polynomial gcd (over or ) Two polynomials of degree with fixed-size integer coefficients One polynomial of degree at most Euclidean algorithm
Fast Euclidean algorithm (Lehmer)[7]

Special functions[edit]

Many of the methods in this section are given in Borwein & Borwein.[8]

Elementary functions[edit]

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.

Algorithm Applicability Complexity
Taylor series; repeated argument reduction (e.g. ) and direct summation
Taylor series; FFT-based acceleration
Taylor series; binary splitting + bit-burst algorithm[9]
Arithmetic–geometric mean iteration[10]

It is not known whether is the optimal complexity for elementary functions. The best known lower bound is the trivial bound .

Non-elementary functions[edit]

Function Input Algorithm Complexity
Gamma function -digit number Series approximation of the incomplete gamma function
Fixed rational number Hypergeometric series
, for integer. Arithmetic-geometric mean iteration
Hypergeometric function -digit number (As described in Borwein & Borwein)
Fixed rational number Hypergeometric series

Mathematical constants[edit]

This table gives the complexity of computing approximations to the given constants to correct digits.

Constant Algorithm Complexity
Golden ratio, Newton's method
Square root of 2, Newton's method
Euler's number, Binary splitting of the Taylor series for the exponential function
Newton inversion of the natural logarithm
Pi, Binary splitting of the arctan series in Machin's formula [11]
Gauss–Legendre algorithm [11]
Euler's constant, Sweeney's method (approximation in terms of the exponential integral)

Number theory[edit]

Algorithms for number theoretical calculations are studied in computational number theory.

Operation Input Output Algorithm Complexity
Greatest common divisor Two -digit integers One integer with at most digits Euclidean algorithm
Binary GCD algorithm
Left/right k-ary binary GCD algorithm[12]
Stehlé–Zimmermann algorithm[13]
Schönhage controlled Euclidean descent algorithm[14]
Jacobi symbol Two -digit integers , or Schönhage controlled Euclidean descent algorithm[15]
Stehlé–Zimmermann algorithm[16]
Factorial A positive integer less than One -digit integer Bottom-up multiplication
Binary splitting
Exponentiation of the prime factors of ,[17]
Primality test A -digit integer True or false AKS primality test [18][19] or ,[20][21]
, assuming Agrawal's conjecture
Elliptic curve primality proving heuristically[22]
Baillie–PSW primality test [23][24]
Miller–Rabin primality test [25]
Solovay–Strassen primality test [25]
Integer factorization A -bit input integer A set of factors General number field sieve [nb 1]
Shor's algorithm , on a quantum computer

Matrix algebra[edit]

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.

Operation Input Output Algorithm Complexity
Matrix multiplication Two matrices One matrix Schoolbook matrix multiplication
Strassen algorithm
Coppersmith–Winograd algorithm (galactic algorithm)
Optimized CW-like algorithms[26][27][28][29] (galactic algorithms)
Matrix multiplication One matrix & one matrix One matrix Schoolbook matrix multiplication
Matrix multiplication One matrix &

one matrix, for some

One matrix Algorithms given in [30] , where upper bounds on are given in [30]
Matrix inversion* One matrix One matrix Gauss–Jordan elimination
Strassen algorithm
Coppersmith–Winograd algorithm
Optimized CW-like algorithms
Singular value decomposition One matrix One matrix,
one matrix, &
one matrix
Bidiagonalization and QR algorithm
One matrix,
one matrix, &
one matrix
Bidiagonalization and QR algorithm
Determinant One matrix One number Laplace expansion
Division-free algorithm[31]
LU decomposition
Bareiss algorithm
Fast matrix multiplication[32]
Back substitution Triangular matrix solutions Back substitution[33]

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.[34]


Algorithms for computing transforms of functions (particularly integral transforms) are widely used in all areas of mathematics, particularly analysis and signal processing.

Operation Input Output Algorithm Complexity
Discrete Fourier transform Finite data sequence of size Set of complex numbers Fast Fourier transform


  1. ^ This form of sub-exponential time is valid for all . A more precise form of the complexity can be given as


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  31. ^
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Further reading[edit]