highly-constrained non-convex optimization and uncertainty quantification
The mystic framework provides a collection of optimization algorithms
and tools that allows the user to more robustly (and readily) solve
optimization problems. All optimization algorithms included in mystic
provide workflow at the fitting layer, not just access to the algorithms
as function calls. mystic gives the user fine-grained power to both
monitor and steer optimizations as the fit processes are running.
Where possible, mystic optimizers share a common interface, and thus can
be easily swapped without the user having to write any new code. mystic
solvers all conform to a solver API, thus also have common method calls
to configure and launch an optimization job. For more details, see
mystic.abstract_solver. The API also makes it easy to bind a favorite
3rd party solver into the mystic framework.
By providing a robust interface designed to allow the user to easily
configure and control solvers, mystic reduces the barrier to implementing
a target fitting problem as stable code. Thus the user can focus on
building their physical models, and not spend time hacking together an
interface to optimization code.
mystic is in active development, so any user feedback, bug reports, comments,
or suggestions are highly appreciated. A list of known issues is maintained
at http://trac.mystic.cacr.caltech.edu/project/mystic/query, with a public
ticket list at https://github.com/uqfoundation/mystic/issues.
mystic provides a stock set of configurable, controllable solvers with::
- a common interface
- the ability to impose solver-independent bounds constraints
- the ability to apply solver-independent monitors
- the ability to configure solver-independent termination conditions
- a control handler yielding: [pause, continue, exit, and callback]
- ease in selecting initial conditions: [guess, random]
- ease in selecting mutation strategies (for differential evolution)
To get up and running quickly, mystic also provides infrastructure to::
- easily generate a fit model (several example models are included)
- configure and auto-generate a cost function from a model
- extend fit jobs to parallel & distributed resources
The latest released version of mystic is available from::
http://trac.mystic.cacr.caltech.edu/project/mystic
mystic is distributed under a 3-clause BSD license.
You can get the latest development version with all the shiny new features at:: https://github.com/uqfoundation
If you have a new contribution, please submit a pull request.
Probably the best way to get started is to look at a few of the
examples provided within mystic. See mystic.examples for a
set of scripts that demonstrate the configuration and launching of
optimization jobs for one of the sample models in mystic.models.
Many of the included examples are standard optimization test problems.
The source code is also generally well documented, so further questions
may be resolved by inspecting the code itself. Please also feel free to
submit a ticket on github, or ask a question on stackoverflow (@Mike McKerns).
mystic is an active research tool. There are a growing number of publications
and presentations that discuss real-world examples and new features of mystic
in greater detail than presented in the user's guide. If you would like to
share how you use mystic in your work, please post a link or send an email
(to mmckerns at uqfoundation dot org).
Instructions on building a new model are in mystic.models.abstract_model.
mystic provides base classes for two types of models::
AbstractFunction[evaluates f(x) for given evaluation points x]AbstractModel[generates f(x,p) for given coefficients p]
It is, however, not necessary to use the base classes in your own model.
mystic also provides some convienence functions to help you build a
model instance and a cost function instance on-the-fly. For more
information, see mystic.forward_model.
All mystic solvers are highly configurable, and provide a robust set of
methods to help customize the solver for your particular optimization
problem. For each solver, a minimal interface is also provided for users
who prefer to configure their solvers in a single function call. For more
information, see mystic.abstract_solver for the solver API, and
each of the individual solvers for their minimal (non-API compliant)
interface.
mystic extends the solver API to parallel computing by providing a solver
class that utilizes the parallel map-reduce algorithm. mystic includes
a set of defaults in mystic.python_map that mirror the behavior
of serial python and the built-in python map function. mystic solvers
built on map-reduce can utilize the distributed and parallel tools provided
by the pathos package, and thus with little new code solvers are
extended to high-performance computing. For more information, see
mystic.abstract_map_solver, mystic.abstract_ensemble_solver,
and the pathos documentation at http://dev.danse.us/trac/pathos.
Important classes and functions are found here::
mystic.solvers[solver optimization algorithms]mystic.termination[solver termination conditions]mystic.strategy[solver population mutation strategies]mystic.monitors[optimization monitors]mystic.tools[function wrappers, etc]mystic.forward_model[cost function generator]mystic.models[a collection of standard models]mystic.math[some mathematical functions and tools]
Solver and model API definitions are found here::
mystic.abstract_solver[the solver API definition]mystic.abstract_map_solver[the parallel solver API]mystic.abstract_ensemble_solver[the ensemble solver API]mystic.models.abstract_model[the model API definition]
If you use mystic to do research that leads to publication, we ask that you
acknowledge use of mystic by citing the following in your publication::
M.M. McKerns, L. Strand, T. Sullivan, A. Fang, M.A.G. Aivazis,
"Building a framework for predictive science", Proceedings of
the 10th Python in Science Conference, 2011;
http://arxiv.org/pdf/1202.1056
Michael McKerns, Patrick Hung, and Michael Aivazis,
"mystic: a simple model-independent inversion framework", 2009- ;
http://trac.mystic.cacr.caltech.edu/project/mystic
Please see http://trac.mystic.cacr.caltech.edu/project/mystic or http://arxiv.org/pdf/1202.1056 for further information.