NUMERICAL METHOD INC Selected as a Red Herring Top 100 Asia Tech Startup
Hong Kong, China - Numerical Method Incorporation Limited has won the Top 100 Asia award. Numerical Method Inc. publishes SuanShu, a Java math library, and AlgoQuant, an algorithmic/quantitative trading strategy research platform.
Red Herring announced its Top 100 Asia award in recognition of the leading private companies from Asia, celebrating these startups’ innovations and technologies across their respective industries.
Red Herring’s Top 100 list has become a mark of distinction for identifying promising new companies and entrepreneurs. Red Herring editors were among the first to recognize that companies such as Facebook, Twitter, Google, Yahoo, Skype, Salesforce.com, YouTube, and eBay would change the way we live and work.
“Choosing the companies with the strongest potential was by no means a small feat,” said Alex Vieux, publisher and CEO of Red Herring. “After rigorous contemplation and discussion, we narrowed our list down from hundreds of candidates from across Asia to the Top 100 Winners. We believe Numerical Method Inc. embodies the vision, drive and innovation that define a successful entrepreneurial venture. Numerical Method Inc. should be proud of its accomplishment, as the competition was very strong.”
Red Herring’s editorial staff evaluated the companies on both quantitative and qualitative criteria, such as financial performance, technology innovation, management quality, strategy, and market penetration. This assessment of potential is complemented by a review of the track record and standing of startups relative to their sector peers, allowing Red Herring to see past the “buzz” and make the list a valuable instrument of discovery and advocacy for the most promising new business models in Asia.
Numerical Method Inc. publishes SuanShu, a Java numerical and statistical library. The objective of SuanShu is to enable very easy programming of engineering applications. Programmers are able to program mathematics in a way that the source code is solidly object-oriented and individually testable. SuanShu source code adheres to the strictest coding standard so that it is readable, maintainable, and can be easily modified and extended.
SuanShu revolutionizes how numerical computing is traditionally done, e.g., netlib, gsl. The repositories of these most popular and somewhat “standard” libraries are rather collections of ad-hoc source code in obsolete languages, e.g., FORTRAN and C. One biggest problem of these code is that they are not readable (for most modern programmers), hence unmaintainable. For example, it is quite a challenge to understand AS 288, let alone improving it. Other problems include, but not limited to, the lack of data structure, duplicated code, being entirely procedural, very bad variable naming, abuse of GOTO, the lack of test cases, insufficient documentations, the lack of IDE support, inconvenient linking to modern languages such as Java, being unfriendly to parallel computing, etc.
To address these problems, SuanShu designs a framework of reusable math components (not procedures) so that programmers can put components together like Legos to build more complex algorithms. SuanShu is written from anew so that it conforms to the modern programming paradigm such as variable naming, code structuring, reusability, readability, maintainability, as well as software engineering procedure. To ensure very high quality of the code and very few bugs, SuanShu has a few thousands of unit test cases that run daily.
The basic of SuanShu covers the following.
- numerical differentiation and integration
- polynomial and Jenkin-Straub
- root finding
- unconstrained and constrained optimization for univariate and multivariate functions
- linear algebra: matrix operations and factorization
- sparse matrix
- descriptive statistics
- random sampling from distributions
Comparing to competing products, SuanShu, as we believe, has the most extensive coverage in statistics. SuanShu covers the following.
- Ordinary Least Square (OLS) regression
- Generalized Linear Model (GLM) regression
- a full suite of residual analysis
- Stochastic Differential Equation (SDE) simulation
- a comprehensive library of hypothesis testing: Kolmogorov-Smirnov, D’Agostino, Jarque-Bera, Lilliefors, Shapiro-Wilk, One-way ANOVA, T, Kruskal-Wallis, Siegel-Tukey, Van der Waerden, Wilcoxon rank sum, Wilcoxon signed rank, Breusch-Pagan, ADF, Bartlett, Brown-Forsythe, F, Levene, Pearson’s Chi-square, Portmanteau
- time series analysis, univariate and multivariate
- ARIMA, GARCH modelling, simulation, fitting, and prediction
- sample and theoretical auto-correlation
- hidden Markov chain
- Kalman filter
For the full article, please read “SuanShu Introduction“.