mystic: a framework for highly-constrained non-convex optimization and uncertainty quantification
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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 the early development stages, and any user feedback is highly appreciated. Contact Mike McKerns [mmckerns at caltech dot edu] with comments, suggestions, and any bugs you may find. A list of known issues is maintained at http://trac.mystic.cacr.caltech.edu/project/mystic/query.
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 user_callback]
- ease in selecting initial conditions: [initial_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
- couple models with optimization parameter constraints [COMING SOON]
Mystic is distributed under a modified BSD license.
If you like living on the edge, and don't mind the promise of a little instability,
you can get the latest development release with all the shiny new features at::
or even better, fork us on our github mirror of the svn trunk::
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
Probably the best way to get started is to look at the tutorial examples provided within the user's guide. The source code is also generally well documented, so further questions may be resolved by inspecting the code itself, or through browsing the reference manual. For those who like to leap before they look, you can jump right to the installation instructions. If the aforementioned documents do not adequately address your needs, please send us feedback.
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 send us a link.