Constrained nonlinear optimization


The mystic framework provides a collection of optimization algorithms and tools that allows the user to more robustly (and easily) solve hard optimization problems for machine learning, uncertainty quantification and AI. mystic gives the user fine-grained power to both monitor and steer optimizations as the fit processes are running. Users can customize optimizer stop conditions, where both compound and user-provided conditions may be used. Optimizers can save state, can be reconfigured dynamically, and can be restarted from a saved solver or from a results file. All solvers can also leverage parallel computing, either within each iteration or as an ensemble of solvers.

mystic provides a stock set of configurable, controllable solvers with:
 * a common interface
 * a control handler with: pause, continue, exit, and callback
 * ease in selecting initial population conditions: guess, random, etc
 * ease in checkpointing and restarting from a log or saved state
 * the ability to leverage parallel & distributed computing
 * the ability to apply a selection of logging and/or verbose monitors
 * the ability to configure solver-independent termination conditions
 * the ability to impose custom and user-defined penalties and constraints

mystic is part of pathos, a Python framework for heterogeneous computing.

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