SuanShu numerical library

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]

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

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 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.
6. Ansari, Mohd Samar (2013-09-03). Non-Linear Feedback Neural Networks: VLSI Implementations and Applications. Springer. ISBN 978-81-322-1563-9.