High Performance on a Rich Feature Set with Zero Dependencies
What’s in ojAlgo?
- ojAlgo is the fastest pure Java linear algebra library available. That statement is backed by the latest Java Matrix Benchmark results – that’s a third party independent benchmark (not written by anyone associated with ojAlgo).
- Optimisation (mathematical programming) tools including LP, QP and MIP solvers – again this is pure Java with zero dependencies.
- A collection of “array” classes that can be sparse or dense and arbitrarily large. They can be used as 1-, 2- or N/Any-dimensional arrays, and may contain/handle a multitude of different number types including complex numbers, rational numbers and quaternions. The memory for the arrays can alternatively be allocated off heap or in a file. The linear algebra part of ojAlgo builds on these arrays – they’re fast and efficient.
- Various other things like time series, random numbers, stochastic processes, descriptive statistics…
Much of the development has been motivated by various financial applications. Up until (v44) ojAlgo contained domain (finance) specific code. With v44 this has been moved to its own code repository and will be released as a separate artefact: ojAlgo-finance
It contains code that allow you to:
- Download historical data from Yahoo Finance or Google Finance
- Patch, transform and analyse time series data
- Construct/optimise portfolios
- Generate future market scenarios
- Simulate the behaviour of portfolios
Everything necessary to do that is in ojAlgo-finance – it has no dependencies other than ojAlgo (the core/base artefact).
BLaadin Edge Financial Systems
BLaadin is a financial market simulation tool. It facilitates market analysis and forecasting as well as making portfolio recommendations. In particular it asserts consistency between forecasts and recommendations. At its core it makes use of the Black-Litterman model.
Information about BLaadin is currently maintained at the BLaadin Edge Financial Systems site.
There is a “public beta” version of BLaadin available on-line as a cloud service. The BLaadin Edge Financial Systems site contains information about how to find and use it.
BLaadin has its origin in a cooperation between Optimatika and the Royal Institute of Technology in Stockholm (KTH). That cooperation aimed to develop a prototype system/application using the Black-Litterman model, and to use that application in a behavioural finance case study. The case study resulted in a PhD thesis.
That was years ago… The prototype died and was revitalised several times until it was finally completely rewritten with a much more ambitious goal. BLaadin is now a full-fledged market simulation and portfolio construction tool.
BLaadin is programmed in Java, and everything related to mathematics and financials is in ojAlgo. BLaadin is NOT open source! ojAlgo is, but not BLaadin. The on-line “public beta” is free for you to try.
ojAlgo has zero dependencies, but through various extension modules it integrates with 3:d party libraries.
ojAlgo is an Open Source project. It is available as source code, and every single source code file contain a copyright statement and a license agreement. All files have the same copyright and license. Optimatika holds the copyright, and the license used is the generous and easy to understand MIT license. Please read and comply with the license agreement – it’s not hard.
- Code for the JVM: There are no calls to native (C or Fortran) libraries. ojAlgo is primarily a Java library, but will, if posible, adapt its code so that other languages that run on the JVM can also use it. (Scala, Android and Kotlin programmers are already sucessfully using ojAlgo.)
- No dependencies: NOTHING besides a JDK is needed to compile or execute the code. In fact ojAlgo only requires the Compact Profile ‘compact1’.
- Write code that scales: When choosing algorithms and data structures ojAlgo often favours designs that perform well on larger more complex cases, even if it’s more than what’s currently required.
- Anything and everything may change in the future (breaking API compatibility). Part of the code is “in production” in commercial systems – but will be changed if improvements require it.
Documentation, Support & Services
User documentation with code examples is at the GitHub wiki. Don’t forget to check out the various README files in the repositories.
Programming questions related to ojAlgo are best asked at stack overflow. Just remember to actually mention ojAlgo and tag the question using ‘ojalgo’ and whatever other tags you find suitable.
Bug reports, and to some extent feature requests should be posted at GitHub Issues. (Please do not use GitHub Issues for general discussions or support requests!)
The ojAlgo-user mailing list can be used for just about anything as long as it relates to ojAlgo. Regarding the list:
- You have to be a member to be able to post to the list – anyone can sign up.
- All new members are moderated until the administrator decides to trust them (usually with their first post).
- There is a searchable list archive. It’s a low volume list, but it’s been around since 2003 so there is content to search and information to find.
ojAlgo is Open Source, and you are strongly encouraged to clone or fork the repository and work directly with the source code. The source code is (part of) the documentation, and you should read it.
Optimatika is a software consulting company specialising in decision support systems – particularly optimisation and applied maths related to finance.
- Optimatika offers commercial support for ojAlgo.
- Optimatika accepts commissions to extend ojAlgo.
- Even without ojAlgo, Optimatika can help you build high performance systems.
In these cases please contact Optimatika directly.
- In the late 90:s the MathWorks and NIST released JAMA, a public-domain Java matrix package reference implementation. It’s still available and still widely used. More so its matrix decomposition implementations have been used (copied) in many other linear algebra packages. ojAlgo also contains code that originates from JAMA. ojAlgo has surpassed JAMA in every way, but probably wouldn’t be here without it. The earliest ojAlgo versions contained simple optimisation algorithms using JAMA for linear algebra.
- Peter Abeles created the Java Matrix Benchmark (to benchmark his EJML against other Java linear algebra packages). ojAlgo was included in the benchmark and right from the start performed fairly well, but the benchmark did identify a few areas where ojAlgo could be improved. By now whether you benchmark numerical accuracy, memory consumption or (cpu time) performance you’ll see that ojAlgo is a top performer.
- For many years now ej-technologies have provided a JProfiler license for the development of ojAlgo. Having an easy to use profiler has been very valuable to the development of ojAlgo. It’s not a tool you use every day, but every time we use it we learn something about our code.