SuanShu numerical library explained

SuanShu
Latest Release Version:	20120606
Latest Release Date:	2012-06-06
Programming Language:	Java
Genre:	Math
License:	Apache License 2.0

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 functionBracketSearchMinimizer solver = new BrentMinimizer(1e-8, 10); // precision, max number of iterationsUnivariateMinimizer.Solution soln = solver.solve(logGamma); // optimizationdouble 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

Notes and References

1. Web site: Java Numerics: Main. 2021-03-23. math.nist.gov.
2. Web site: 2015-08-07. Fastest Java Matrix Multiplication NM DEV. 2021-08-02. NM DEV Mathematics at Your Fingertips.
3. Automatic stability verification via Lyapunov functions: representations, transformations, and practical issues. Universität Oldenburg.. 2018. phd. Eike. Möhlmann.
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. en. 40. 10. 2387–2397. 10.1016/j.cor.2013.04.009. 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. en. 16. 1. 40. 10.17951/ai.2016.16.1.40. 2083-3628. free.
6. Book: Ansari, Mohd Samar. Non-Linear Feedback Neural Networks: VLSI Implementations and Applications. 2013-09-03. Springer. 978-81-322-1563-9. en.