SuanShu numerical library

Last updated
SuanShu
Stable release
20120606 / 2012-06-06
Written in Java
Type Math
License Apache License 2.0
Website github.com/nmltd/SuanShu

SuanShu is a Java math library. It is open-source under Apache License 2.0 available in GitHub. SuanShu is a large collection of Java classes for basic numerical analysis, statistics, and optimization. [1] It implements a parallel version of the adaptive strassen's algorithm for fast matrix multiplication. [2] SuanShu has been quoted and used in a number of academic works. [3] [4] [5] [6]

Contents

Features

License terms

SuanShu is released under the terms of the Apache License 2.0

Examples of usage

The following code shows the object-oriented design of the library (in contrast to the traditional procedural design of many other FORTRAN and C numerical libraries) by a simple example of minimization.

LogGammalogGamma=newLogGamma();// the log-gamma functionBracketSearchMinimizersolver=newBrentMinimizer(1e-8,10);// precision, max number of iterationsUnivariateMinimizer.Solutionsoln=solver.solve(logGamma);// optimizationdoublex_min=soln.search(0,5);// bracket = [0, 5]System.out.println(String.format("f(%f) = %f",x_min,logGamma.evaluate(x_min)));

See also

References

  1. ↑ "Java Numerics: Main". math.nist.gov. Retrieved 2021-03-23.
  2. ↑ "Fastest Java Matrix Multiplication | NM DEV". NM DEV | Mathematics at Your Fingertips. 2015-08-07. Retrieved 2021-08-02.
  3. ↑ Möhlmann, Eike (2018). Automatic stability verification via Lyapunov functions: representations, transformations, and practical issues (phd thesis). Universität Oldenburg.
  4. ↑ Christou, Ioannis T.; Vassilaras, Spyridon (2013-10-01). "A parallel hybrid greedy branch and bound scheme for the maximum distance-2 matching problem" . Computers & Operations Research. 40 (10): 2387–2397. doi:10.1016/j.cor.2013.04.009. ISSN   0305-0548.
  5. ↑ Łukawska, Barbara; Łukawski, Grzegorz; Sapiecha, Krzysztof (2016-10-04). "An implementation of artificial advisor for dynamic classification of objects". Annales Universitatis Mariae Curie-Sklodowska, sectio AI – Informatica. 16 (1): 40. doi: 10.17951/ai.2016.16.1.40 . ISSN   2083-3628.
  6. ↑ Ansari, Mohd Samar (2013-09-03). Non-Linear Feedback Neural Networks: VLSI Implementations and Applications. Springer. ISBN   978-81-322-1563-9.
This page is based on this Wikipedia article
Text is available under the CC BY-SA 4.0 license; additional terms may apply.
Images, videos and audio are available under their respective licenses.