Version 1.4.0 of the dysii Dynamic Systems Library has been released. This is a major new release with a number of additional features and performance enhancements, as well as representing a consolidation of code and maturation of much of the API.
Particular new features include:
- The kernel forward-backward and two-filter smoothers, suitable for fast, large-scale approximate inference in continuous-time stochastic models, as documented in my recent PhD thesis.
- Overhauled kd tree implementation, featuring distributed partitioning, dual-tree and self-tree evaluations, particularly useful for the new smoothers above.
- Improved stochastic Runge-Kutta and new Euler-Maruyama method for integrating stochastic differential equations.
- Performance improvements resulting from continued profiling, including more aggressive inlining and less dependence on virtuals.
- A new installation guide, available in the INSTALL.txt file of the distribution. Also note that with Boost 1.35 now released, dysii no longer requires the latest CVS of Boost, making it much simpler to install.
Full details are included in the VERSION.txt file of the distribution.
A couple of examples of applications using dysii are expected to be released within a matter of days also. These should provide an excellent starting point for those wishing to use the library for their own work.