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. It implements a parallel version of the adaptive strassen's algorithm for fast matrix multiplication. SuanShu has been quoted and used in a number of academic works.
Features
- linear algebra
- root finding
- curve fitting and interpolation
- unconstrained and constrained optimization
- statistical analysis
- linear regression
- probability distributions and random number generation
- ordinary and partial differential equation solvers
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.
LogGamma logGamma = new LogGamma(); // the log-gamma function BracketSearchMinimizer solver = new BrentMinimizer(1e-8, 10); // precision, max number of iterations UnivariateMinimizer.Solution soln = solver.solve(logGamma); // optimization double x_min = soln.search(0, 5); // bracket = [0, 5] System.out.println(String.format("f(%f) = %f", x_min, logGamma.evaluate(x_min)));See also
- SOCP - Explanation of Second Order Conic Programming
- SDP - Explanation of Semidefinite Programming
- SQP - Explanation of Sequential quadratic programming
- Interior Point Method
- Adaptive strassen's algorithm – fast matrix multiplication
- Apache License 2.0 - Version 2 of the Apache Software License
References
"Java Numerics: Main". math.nist.gov. Retrieved 2021-03-23. https://math.nist.gov/javanumerics/ ↩
"Fastest Java Matrix Multiplication | NM DEV". NM DEV | Mathematics at Your Fingertips. 2015-08-07. Retrieved 2021-08-02. https://nm.dev/2015/08/07/accelerating-matrix-multiplication/ ↩
Möhlmann, Eike (2018). Automatic stability verification via Lyapunov functions: representations, transformations, and practical issues (phd thesis). Universität Oldenburg. http://oops.uni-oldenburg.de/4217/ ↩
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. https://www.sciencedirect.com/science/article/pii/S0305054813001159 ↩
Łukawska, Barbara; Łukawski, Grzegorz; Sapiecha, Krzysztof (2016-10-04). "An implementation of articial advisor for dynamic classication of objects". Annales Universitatis Mariae Curie-Sklodowska, sectio AI – Informatica. 16 (1): 40. doi:10.17951/ai.2016.16.1.40. ISSN 2083-3628. https://reshistorica.journals.umcs.pl/ai/article/view/3502 ↩
Ansari, Mohd Samar (2013-09-03). Non-Linear Feedback Neural Networks: VLSI Implementations and Applications. Springer. ISBN 978-81-322-1563-9. 978-81-322-1563-9 ↩