Computational complexity of mathematical operations

Graphs of functions commonly used in the analysis of algorithms, showing the number of operations ${\displaystyle N}$ versus input size ${\displaystyle n}$ 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, ${\displaystyle M(n)}$ below stands in for the complexity of the chosen multiplication algorithm.

Arithmetic functions

This table lists the complexity of mathematical operations on integers.

Operation	Input	Output	Algorithm	Complexity
Addition	Two ${\displaystyle n}$-digit numbers	One ${\displaystyle n+1}$-digit number	Schoolbook addition with carry	${\displaystyle \Theta (n)}$
Subtraction	Two ${\displaystyle n}$-digit numbers	One ${\displaystyle n}$-digit number	Schoolbook subtraction with borrow	${\displaystyle \Theta (n)}$
Multiplication	Two ${\displaystyle n}$-digit numbers	One ${\displaystyle 2n}$-digit number	Schoolbook long multiplication	${\displaystyle O{\mathord {\left(n^{2}\right)}}}$
			Karatsuba algorithm	${\displaystyle O{\mathord {\left(n^{1.585}\right)}}}$
			3-way Toom–Cook multiplication	${\displaystyle O{\mathord {\left(n^{1.465}\right)}}}$
			${\displaystyle k}$-way Toom–Cook multiplication	${\displaystyle O{\mathord {\left(n^{\frac {\log(2k-1)}{\log k}}\right)}}}$
			Mixed-level Toom–Cook (Knuth 4.3.3-T) [2]	${\displaystyle O{\mathord {\left(n\,2^{\sqrt {2\log n}}\,\log n\right)}}}$
			Schönhage–Strassen algorithm	${\displaystyle O{\mathord {\left(n\log n\log \log n\right)}}}$
			Harvey-Hoeven algorithm [3] [4]	${\displaystyle O(n\log n)}$
Division	Two ${\displaystyle n}$-digit numbers	One ${\displaystyle n}$-digit number	Schoolbook long division	${\displaystyle O{\mathord {\left(n^{2}\right)}}}$
			Burnikel–Ziegler Divide-and-Conquer Division [5]	${\displaystyle O(M(n)\log n)}$
			Newton–Raphson division	${\displaystyle O(M(n))}$
Square root	One ${\displaystyle n}$-digit number	One ${\displaystyle n/2}$-digit number	Newton's method	${\displaystyle O(M(n))}$
Modular exponentiation	Two ${\displaystyle n}$-digit integers and a ${\displaystyle k}$-bit exponent	One ${\displaystyle n}$-digit integer	Repeated multiplication and reduction	${\displaystyle O{\mathord {\left(M(n)\,2^{k}\right)}}}$
			Exponentiation by squaring	${\displaystyle O(M(n)\,k)}$
			Exponentiation with Montgomery reduction	${\displaystyle O(M(n)\,k)}$

On stronger computational models, specifically a pointer machine and consequently also a unit-cost random-access machine it is possible to multiply two n-bit numbers in time O(n). ^[6]

Algebraic functions

Here we consider operations over polynomials and n denotes their degree; for the coefficients we use a unit-cost model, ignoring the number of bits in a number. In practice this means that we assume them to be machine integers. For this section ${\displaystyle M(n)}$ indicates the time needed for multiplying two polynomials of degree at most ${\displaystyle n}$. ^{[7] : 242}

Operation	Input	Output	Algorithm	Complexity
Polynomial evaluation	One polynomial of degree ${\displaystyle n}$ with integer coefficients	One number	Direct evaluation	${\displaystyle \Theta (n)}$
			Horner's method	${\displaystyle \Theta (n)}$
Polynomial multipoint evaluation	One polynomial of degree less than ${\displaystyle n}$ with integer coefficients and ${\displaystyle n}$ numbers as evaluation points	${\displaystyle n}$ numbers	Direct evaluation	${\displaystyle \Theta (n^{2})}$
			Fast multipoint evaluation [7] : 295	${\displaystyle O(M(n)\log n)}$
Polynomial gcd (over ${\displaystyle \mathbb {Z} [x]}$ or ${\displaystyle F[x]}$)	Two polynomials of degree ${\displaystyle n}$ with integer coefficients	One polynomial of degree at most ${\displaystyle n}$	Euclidean algorithm	${\displaystyle O{\mathord {\left(n^{2}\right)}}}$
			Fast Euclidean algorithm [7] : 318  (Lehmer [7] : 324 )	${\displaystyle O(M(n)\log n)}$

Special functions

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

Elementary functions

The elementary functions are constructed by composing arithmetic operations, the exponential function (${\displaystyle \exp }$), the natural logarithm (${\displaystyle \log }$), trigonometric functions (${\displaystyle \sin ,\cos }$), 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 ${\displaystyle \exp }$ or ${\displaystyle \log }$ in the complex domain can be computed with some complexity, then that complexity is attainable for all other elementary functions.

Below, the size ${\displaystyle n}$ 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. ${\displaystyle \exp(2x)=[\exp(x)]^{2}}$) and direct summation	${\displaystyle \exp ,\log ,\sin ,\cos ,\arctan }$	${\displaystyle O{\mathord {\left(M(n)n^{1/2}\right)}}}$
Taylor series; FFT-based acceleration	${\displaystyle \exp ,\log ,\sin ,\cos ,\arctan }$	${\displaystyle O{\mathord {\left(M(n)n^{1/3}(\log n)^{2}\right)}}}$
Taylor series; binary splitting + bit-burst algorithm [9]	${\displaystyle \exp ,\log ,\sin ,\cos ,\arctan }$	${\displaystyle O{\mathord {\left(M(n)(\log n)^{2}\right)}}}$
Arithmetic–geometric mean iteration [10]	${\displaystyle \exp ,\log ,\sin ,\cos ,\arctan }$	${\displaystyle O(M(n)\log n)}$

It is not known whether ${\displaystyle O(M(n)\log n)}$ is the optimal complexity for elementary functions. The best known lower bound is the trivial bound ${\displaystyle \Omega }$ ${\displaystyle (M(n))}$.

Non-elementary functions

Function	Input	Algorithm	Complexity
Gamma function	${\displaystyle n}$-digit number	Series approximation of the incomplete gamma function	${\displaystyle O{\mathord {\left(M(n)n^{1/2}(\log n)^{2}\right)}}}$
	Fixed rational number	Hypergeometric series	${\displaystyle O{\mathord {\left(M(n)(\log n)^{2}\right)}}}$
	${\displaystyle m/24}$, for ${\displaystyle m}$ integer.	Arithmetic-geometric mean iteration	${\displaystyle O(M(n)\log n)}$
Hypergeometric function ${\displaystyle {}_{p}\!F_{q}}$	${\displaystyle n}$-digit number	(As described in Borwein & Borwein)	${\displaystyle O{\mathord {\left(M(n)n^{1/2}(\log n)^{2}\right)}}}$
	Fixed rational number	Hypergeometric series	${\displaystyle O{\mathord {\left(M(n)(\log n)^{2}\right)}}}$

Mathematical constants

This table gives the complexity of computing approximations to the given constants to ${\displaystyle n}$ correct digits.

Constant	Algorithm	Complexity
Golden ratio, ${\displaystyle \phi }$	Newton's method	${\displaystyle O(M(n))}$
Square root of 2, ${\displaystyle {\sqrt {2}}}$	Newton's method	${\displaystyle O(M(n))}$
Euler's number, ${\displaystyle e}$	Binary splitting of the Taylor series for the exponential function	${\displaystyle O(M(n)\log n)}$
	Newton inversion of the natural logarithm	${\displaystyle O(M(n)\log n)}$
Pi, ${\displaystyle \pi }$	Binary splitting of the arctan series in Machin's formula	${\displaystyle O{\mathord {\left(M(n)(\log n)^{2}\right)}}}$ [11]
	Gauss–Legendre algorithm	${\displaystyle O(M(n)\log n)}$ [11]
Euler's constant, ${\displaystyle \gamma }$	Sweeney's method (approximation in terms of the exponential integral)	${\displaystyle O{\mathord {\left(M(n)(\log n)^{2}\right)}}}$

Number theory

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

Operation	Input	Output	Algorithm	Complexity
Greatest common divisor	Two ${\displaystyle n}$-digit integers	One integer with at most ${\displaystyle n}$ digits	Euclidean algorithm	${\displaystyle O{\mathord {\left(n^{2}\right)}}}$
			Binary GCD algorithm	${\displaystyle O{\mathord {\left(n^{2}\right)}}}$
			Left/right k-ary binary GCD algorithm [12]	${\displaystyle O{\mathord {\left({\frac {n^{2}}{\log n}}\right)}}}$
			Stehlé–Zimmermann algorithm [13]	${\displaystyle O(M(n)\log n)}$
			Schönhage controlled Euclidean descent algorithm [14]	${\displaystyle O(M(n)\log n)}$
Jacobi symbol	Two ${\displaystyle n}$-digit integers	${\displaystyle 0}$, ${\displaystyle -1}$ or ${\displaystyle 1}$	Schönhage controlled Euclidean descent algorithm [15]	${\displaystyle O(M(n)\log n)}$
			Stehlé–Zimmermann algorithm [16]	${\displaystyle O(M(n)\log n)}$
Factorial	A positive integer less than ${\displaystyle m}$	One ${\displaystyle O(m\log m)}$-digit integer	Bottom-up multiplication	${\displaystyle O{\mathord {\left(M\left(m^{2}\right)\log m\right)}}}$
			Binary splitting	${\displaystyle O(M(m\log m)\log m)}$
			Exponentiation of the prime factors of ${\displaystyle m}$	${\displaystyle O(M(m\log m)\log \log m)}$, [17] ${\displaystyle O(M(m\log m))}$ [1]
Primality test	A ${\displaystyle n}$-digit integer	True or false	AKS primality test	${\displaystyle O{\mathord {\left(n^{6+o(1)}\right)}}}$ [18] [19] ${\displaystyle O(n^{3})}$, assuming Agrawal's conjecture
			Elliptic curve primality proving	${\displaystyle O{\mathord {\left(n^{4+\varepsilon }\right)}}}$heuristically [20]
			Baillie–PSW primality test	${\displaystyle O{\mathord {\left(n^{2+\varepsilon }\right)}}}$ [21] [22]
			Miller–Rabin primality test	${\displaystyle O{\mathord {\left(kn^{2+\varepsilon }\right)}}}$ [23]
			Solovay–Strassen primality test	${\displaystyle O{\mathord {\left(kn^{2+\varepsilon }\right)}}}$ [23]
Integer factorization	A ${\displaystyle b}$-bit input integer	A set of factors	General number field sieve	${\displaystyle O{\mathord {\left((1+\varepsilon )^{b}\right)}}}$ [nb 1]
			Shor's algorithm	${\displaystyle O(M(b)b)}$, on a quantum computer

Matrix algebra

Main article: 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.

Operation	Input	Output	Algorithm	Complexity
Matrix multiplication	Two ${\displaystyle n\times n}$ matrices	One ${\displaystyle n\times n}$ matrix	Schoolbook matrix multiplication	${\displaystyle O(n^{3})}$
			Strassen algorithm	${\displaystyle O{\mathord {\left(n^{2.807}\right)}}}$
			Coppersmith–Winograd algorithm (galactic algorithm)	${\displaystyle O{\mathord {\left(n^{2.376}\right)}}}$
			Optimized CW-like algorithms [24] [25] [26] [27] (galactic algorithms)	${\displaystyle O{\mathord {\left(n^{\psi =2.3728596}\right)}}}$
	One ${\displaystyle n\times m}$ matrix, and one ${\displaystyle m\times p}$ matrix	One ${\displaystyle n\times p}$ matrix	Schoolbook matrix multiplication	${\displaystyle O(nmp)}$
	One ${\displaystyle n\times \left\lceil n^{k}\right\rceil }$ matrix, and one ${\displaystyle \left\lceil n^{k}\right\rceil \times n}$ matrix, for some ${\displaystyle k\geq 0}$	One ${\displaystyle n\times n}$ matrix	Algorithms given in [28]	${\displaystyle O(n^{\omega (k)+\epsilon })}$, where upper bounds on ${\displaystyle \omega (k)}$ are given in [28]
Matrix inversion	One ${\displaystyle n\times n}$ matrix	One ${\displaystyle n\times n}$ matrix	Gauss–Jordan elimination	${\displaystyle O{\mathord {\left(n^{3}\right)}}}$
			Strassen algorithm	${\displaystyle O{\mathord {\left(n^{2.807}\right)}}}$
			Coppersmith–Winograd algorithm	${\displaystyle O{\mathord {\left(n^{2.376}\right)}}}$
			Optimized CW-like algorithms	${\displaystyle O{\mathord {\left(n^{\psi }\right)}}}$
Singular value decomposition	One ${\displaystyle m\times n}$ matrix	One ${\displaystyle m\times m}$ matrix, one ${\displaystyle m\times n}$ matrix, & one ${\displaystyle n\times n}$ matrix	Bidiagonalization and QR algorithm	${\displaystyle O{\mathord {\left(m^{2}n\right)}}}$ (${\displaystyle m\geq n}$)
		One ${\displaystyle m\times n}$ matrix, one ${\displaystyle n\times n}$ matrix, & one ${\displaystyle n\times n}$ matrix	Bidiagonalization and QR algorithm	${\displaystyle O{\mathord {\left(mn^{2}\right)}}}$ (${\displaystyle m\leq n}$)
QR decomposition	One ${\displaystyle m\times n}$ matrix	One ${\displaystyle m\times n}$ matrix, & one ${\displaystyle n\times n}$ matrix	Algorithms in [29]	${\displaystyle O{\mathord {\left(mn^{1+{\frac {1}{4-\omega }}}\right)}}}$ (${\displaystyle m\geq n}$)
Determinant	One ${\displaystyle n\times n}$ matrix	One number	Laplace expansion	${\displaystyle O(n!)}$
			Division-free algorithm [30]	${\displaystyle O{\mathord {\left(n^{4}\right)}}}$
			LU decomposition	${\displaystyle O(n^{3})}$
			Bareiss algorithm	${\displaystyle O{\mathord {\left(n^{3}\right)}}}$
			Fast matrix multiplication [31]	${\displaystyle O{\mathord {\left(n^{\psi }\right)}}}$
Back substitution	Triangular matrix	${\displaystyle n}$ solutions	Back substitution [32]	${\displaystyle O{\mathord {\left(n^{2}\right)}}}$
Characteristic polynomial	One ${\displaystyle n\times n}$ matrix	One degree-${\displaystyle n}$ polynomial	Faddeev-LeVerrier algorithm	${\displaystyle O(n^{\psi +1})}$
			Samuelson-Berkowitz algorithm	${\displaystyle O(n^{\psi +1})}$ (smaller constant factor)
			Preparata-Sarwate algorithm [33] [34]	${\displaystyle O(n^{\psi +1/2}+n^{3})}$

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. ^[35]

Transforms

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 ${\displaystyle n}$	Set of complex numbers	Schoolbook	${\displaystyle O(n^{2})}$
			Fast Fourier transform	${\displaystyle O(n\log n)}$

Notes

1. This form of sub-exponential time is valid for all ${\displaystyle \varepsilon >0}$. A more precise form of the complexity can be given as ${\displaystyle O{\mathord {\left(\exp {\sqrt[{3}]{{\frac {64}{9}}b(\log b)^{2}}}\right)}}.}$

References

1. Schönhage, A.; Grotefeld, A.F.W.; Vetter, E. (1994). Fast Algorithms—A Multitape Turing Machine Implementation. BI Wissenschafts-Verlag. ISBN   978-3-411-16891-0. OCLC   897602049.
2. Knuth 1997
3. Harvey, D.; Van Der Hoeven, J. (2021). "Integer multiplication in time O (n log n)" (PDF). Annals of Mathematics. 193 (2): 563–617. doi:10.4007/annals.2021.193.2.4. S2CID   109934776.
4. Klarreich, Erica (December 2019). "Multiplication hits the speed limit". Commun. ACM. 63 (1): 11–13. doi:10.1145/3371387. S2CID   209450552.
5. Burnikel, Christoph; Ziegler, Joachim (1998). Fast Recursive Division. Forschungsberichte des Max-Planck-Instituts für Informatik. Saarbrücken: MPI Informatik Bibliothek & Dokumentation. OCLC   246319574. MPII-98-1-022.
6. Schönhage, Arnold (1980). "Storage Modification Machines". SIAM Journal on Computing. 9 (3): 490–508. doi:10.1137/0209036.
7. von zur Gathen, J.; Gerhard, J. (2013). Modern Computer Algebra (3rd ed.). Cambridge University Press. ISBN   9781139856065.
8. Borwein, J.; Borwein, P. (1987). Pi and the AGM: A Study in Analytic Number Theory and Computational Complexity. Wiley. ISBN   978-0-471-83138-9. OCLC   755165897.
9. Chudnovsky, David; Chudnovsky, Gregory (1988). "Approximations and complex multiplication according to Ramanujan". Ramanujan revisited: Proceedings of the Centenary Conference. Academic Press. pp. 375–472. ISBN   978-0-01-205856-5.
10. Brent, Richard P. (2014) [1975]. "Multiple-precision zero-finding methods and the complexity of elementary function evaluation". In Traub, J.F. (ed.). Analytic Computational Complexity. Elsevier. pp. 151–176. arXiv: 1004.3412 . ISBN   978-1-4832-5789-1.
11. Richard P. Brent (2020), The Borwein Brothers, Pi and the AGM, Springer Proceedings in Mathematics & Statistics, vol. 313, arXiv: 1802.07558 , doi:10.1007/978-3-030-36568-4, ISBN   978-3-030-36567-7, S2CID   214742997
12. Sorenson, J. (1994). "Two Fast GCD Algorithms". Journal of Algorithms. 16 (1): 110–144. doi:10.1006/jagm.1994.1006.
13. Crandall, R.; Pomerance, C. (2005). "Algorithm 9.4.7 (Stehlé-Zimmerman binary-recursive-gcd)". Prime Numbers – A Computational Perspective (2nd ed.). Springer. pp. 471–3. ISBN   978-0-387-28979-3.
14. Möller N (2008). "On Schönhage's algorithm and subquadratic integer gcd computation" (PDF). Mathematics of Computation. 77 (261): 589–607. Bibcode:2008MaCom..77..589M. doi: 10.1090/S0025-5718-07-02017-0 .
15. Bernstein, D.J. "Faster Algorithms to Find Non-squares Modulo Worst-case Integers".
16. Brent, Richard P.; Zimmermann, Paul (2010). "An ${\displaystyle O(M(n)\log n)}$ algorithm for the Jacobi symbol". International Algorithmic Number Theory Symposium. Springer. pp. 83–95. arXiv: 1004.2091 . doi:10.1007/978-3-642-14518-6_10. ISBN   978-3-642-14518-6. S2CID   7632655.
17. Borwein, P. (1985). "On the complexity of calculating factorials". Journal of Algorithms. 6 (3): 376–380. doi:10.1016/0196-6774(85)90006-9.
18. Lenstra jr., H.W.; Pomerance, Carl (2019). "Primality testing with Gaussian periods" (PDF). Journal of the European Mathematical Society . 21 (4): 1229–69. doi:10.4171/JEMS/861. hdl:21.11116/0000-0005-717D-0.
19. Tao, Terence (2010). "1.11 The AKS primality test". An epsilon of room, II: Pages from year three of a mathematical blog. Graduate Studies in Mathematics. Vol. 117. American Mathematical Society. pp. 82–86. doi:10.1090/gsm/117. ISBN   978-0-8218-5280-4. MR   2780010.
20. Morain, F. (2007). "Implementing the asymptotically fast version of the elliptic curve primality proving algorithm". Mathematics of Computation . 76 (257): 493–505. arXiv: math/0502097 . Bibcode:2007MaCom..76..493M. doi:10.1090/S0025-5718-06-01890-4. MR   2261033. S2CID   133193.
21. Pomerance, Carl; Selfridge, John L.; Wagstaff, Jr., Samuel S. (July 1980). "The pseudoprimes to 25·10⁹" (PDF). Mathematics of Computation. 35 (151): 1003–26. doi: 10.1090/S0025-5718-1980-0572872-7 . JSTOR   2006210.
22. Baillie, Robert; Wagstaff, Jr., Samuel S. (October 1980). "Lucas Pseudoprimes" (PDF). Mathematics of Computation. 35 (152): 1391–1417. doi: 10.1090/S0025-5718-1980-0583518-6 . JSTOR   2006406. MR   0583518.
23. Monier, Louis (1980). "Evaluation and comparison of two efficient probabilistic primality testing algorithms". Theoretical Computer Science . 12 (1): 97–108. doi: 10.1016/0304-3975(80)90007-9 . MR   0582244.
24. Alman, Josh; Williams, Virginia Vassilevska (2020), "A Refined Laser Method and Faster Matrix Multiplication", 32nd Annual ACM-SIAM Symposium on Discrete Algorithms (SODA 2021), pp. 522–539, arXiv: 2010.05846 , doi:10.1137/1.9781611976465.32, ISBN   978-1-61197-646-5, S2CID   222290442
25. Davie, A.M.; Stothers, A.J. (2013), "Improved bound for complexity of matrix multiplication", Proceedings of the Royal Society of Edinburgh, 143A (2): 351–370, doi:10.1017/S0308210511001648, S2CID   113401430
26. Vassilevska Williams, Virginia (2014), Breaking the Coppersmith-Winograd barrier: Multiplying matrices in O(n^2.373) time
27. Le Gall, François (2014), "Powers of tensors and fast matrix multiplication", Proceedings of the 39th International Symposium on Symbolic and Algebraic Computation — ISSAC '14, p. 23, arXiv: 1401.7714 , Bibcode:2014arXiv1401.7714L, doi:10.1145/2608628.2627493, ISBN   9781450325011, S2CID   353236
28. Le Gall, François; Urrutia, Floren (2018). "Improved Rectangular Matrix Multiplication using Powers of the Coppersmith-Winograd Tensor". In Czumaj, Artur (ed.). Proceedings of the Twenty-Ninth Annual ACM-SIAM Symposium on Discrete Algorithms. Society for Industrial and Applied Mathematics. doi:10.1137/1.9781611975031.67. ISBN   978-1-61197-503-1. S2CID   33396059.
29. Knight, Philip A. (May 1995). "Fast rectangular matrix multiplication and QR decomposition". Linear Algebra and Its Applications. 221: 69–81. doi: 10.1016/0024-3795(93)00230-w . ISSN   0024-3795.
30. Rote, G. (2001). "Division-free algorithms for the determinant and the pfaffian: algebraic and combinatorial approaches" (PDF). Computational discrete mathematics. Springer. pp. 119–135. ISBN   3-540-45506-X.
31. Aho, Alfred V.; Hopcroft, John E.; Ullman, Jeffrey D. (1974). "Theorem 6.6". The Design and Analysis of Computer Algorithms. Addison-Wesley. p. 241. ISBN   978-0-201-00029-0.
32. Fraleigh, J.B.; Beauregard, R.A. (1987). Linear Algebra (3rd ed.). Addison-Wesley. p. 95. ISBN   978-0-201-15459-7.
33. Preparata, F.P.; Sarwate, D.V. (April 1978). "An improved parallel processor bound in fast matrix inversion". Information Processing Letters. 7 (3): 148–150. doi: 10.1016/0020-0190(78)90079-0 .
34. Galil, Zvi; Pan, Victor (January 16, 1989). "Parallel evaluation of the determinant and of the inverse of a matrix". Information Processing Letters. 30 (1): 148–150. doi: 10.1016/0020-0190(89)90173-7 ., in which the ${\displaystyle O(n^{3})}$ term is reduced
35. Cohn, Henry; Kleinberg, Robert; Szegedy, Balazs; Umans, Chris (2005). "Group-theoretic Algorithms for Matrix Multiplication". Proceedings of the 46th Annual Symposium on Foundations of Computer Science. IEEE. pp. 379–388. arXiv: math.GR/0511460 . doi:10.1109/SFCS.2005.39. ISBN   0-7695-2468-0. S2CID   6429088.

Further reading

• Brent, Richard P.; Zimmermann, Paul (2010). Modern Computer Arithmetic. Cambridge University Press. ISBN   978-0-521-19469-3.
• Knuth, Donald Ervin (1997). Seminumerical Algorithms. The Art of Computer Programming. Vol. 2 (3rd ed.). Addison-Wesley. ISBN   978-0-201-89684-8.