Mpmath is a pure-Python library for arbitrary-precision floating-point arithmetic. It implements the standard functions from Python’s math and cmath modules (exp, log, sin, etc.), plus a few nonelementary special functions (gamma, zeta, etc.), and has utilities for arbitrary-precision numerical differentiation, integration, root-finding, and interval arithmetic. It supports unlimited exponent sizes, has full support for complex numbers, and offers better performance than Python’s standard decimal library. Mpmath is lightweight and easy to install or include in other software due to being written entirely in Python with no additional dependencies. (Source:

References in zbMATH (referenced in 40 articles )

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  1. Dubashinskiy, Mikhail: Infinite ascension limit: horocyclic chaos (2021)
  2. Akbary, Amir; Francis, Forrest J.: Euler’s function on products of primes in a fixed arithmetic progression (2020)
  3. Barthélémy, J.-F.; Bignonnet, Francois: The Eshelby problem of the confocal (N)-layer spheroid with imperfect interfaces and the notion of equivalent particle in thermal conduction (2020)
  4. Johansson, Fredrik: Computing the Lambert (W) function in arbitrary-precision complex interval arithmetic (2020)
  5. Matthew Roughan: The Polylogarithm Function in Julia (2020) arXiv
  6. Messerschmidt, Miek: On compact packings of the plane with circles of three radii (2020)
  7. Nigam, Nilima; Siudeja, Bartłomiej; Young, Benjamin: A proof via finite elements for Schiffer’s conjecture on a regular pentagon (2020)
  8. Perlov, Leonid: (\mathrmSU(2)) and (\mathrmSU(1, 1)) (Y)-maps in loop quantum gravity (2020)
  9. Chen, Hao: Minimal twin surfaces (2019)
  10. Codenotti, Luca; Lewicka, Marta: Visualization of the convex integration solutions to the Monge-Ampère equation (2019)
  11. Comsa, Iulia M.; Firsching, Moritz; Fischbacher, Thomas: SO(8) supergravity and the magic of machine learning (2019)
  12. Fasi, Massimiliano; Higham, Nicholas J.: An arbitrary precision scaling and squaring algorithm for the matrix exponential (2019)
  13. Giesl, Jürgen; Giesl, Peter; Hark, Marcel: Computing expected runtimes for constant probability programs (2019)
  14. Herbach, Ulysse: Stochastic gene expression with a multistate promoter: breaking down exact distributions (2019)
  15. Johansson, Fredrik: Computing hypergeometric functions rigorously (2019)
  16. Johansson, Fredrik; Blagouchine, Iaroslav V.: Computing Stieltjes constants using complex integration (2019)
  17. Steven G. Murray, Francis J. Poulin: hankel: A Python library for performing simple and accurate Hankel transformations (2019) arXiv
  18. Fasi, Massimiliano; Higham, Nicholas J.: Multiprecision algorithms for computing the matrix logarithm (2018)
  19. Gujarati, Arpan; Nasri, Mitra; Brandenburg, Björn B.: Quantifying the resiliency of fail-operational real-time networked control systems (2018)
  20. Johansson, Fredrik: Numerical integration in arbitrary-precision ball arithmetic (2018)

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