The default algorithm options#
- optimagic.optimization.algo_options.CONVERGENCE_FTOL_REL = 2e-09#
Stop when the relative improvement between two iterations is below this.
The exact definition of relative improvement depends on the optimizer and should be documented there. To disable it, set it to 0.
The default value is inspired by scipy L-BFGS-B defaults, but rounded.
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- optimagic.optimization.algo_options.CONVERGENCE_FTOL_ABS = 0#
Stop when the absolute improvement between two iterations is below this.
Disabled by default because it is very problem specific.
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- optimagic.optimization.algo_options.CONVERGENCE_GTOL_ABS = 1e-05#
Stop when the gradient are smaller than this.
For some algorithms this criterion refers to all entries, for others to some norm.
For bound constrained optimizers this typically refers to a projected gradient. The exact definition should be documented for each optimizer.
The default is the same as scipy. To disable it, set it to zero.
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- optimagic.optimization.algo_options.CONVERGENCE_GTOL_REL = 1e-08#
Stop when the gradient, divided by the absolute value of the criterion function is smaller than this. For some algorithms this criterion refers to all entries, for others to some norm.For bound constrained optimizers this typically refers to a projected gradient. The exact definition should be documented for each optimizer. To disable it, set it to zero.
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- optimagic.optimization.algo_options.CONVERGENCE_GTOL_SCALED = 1e-08#
Stop when all entries (or for some algorithms the norm) of the gradient, divided by the norm of the gradient at start parameters is smaller than this. For bound constrained optimizers this typically refers to a projected gradient. The exact definition should be documented for each optimizer. To disable it, set it to zero.
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- optimagic.optimization.algo_options.CONVERGENCE_XTOL_REL = 1e-05#
Stop when the relative change in parameters is smaller than this. The exact definition of relative change and whether this refers to the maximum change or the average change depends on the algorithm and should be documented there. To disable it, set it to zero. The default is the same as in scipy.
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- optimagic.optimization.algo_options.CONVERGENCE_XTOL_ABS = 0#
Stop when the absolute change in parameters between two iterations is smaller than this. Whether this refers to the maximum change or the average change depends on the algorithm and should be documented there.
Disabled by default because it is very problem specific. To enable it, set it to a value larger than zero.
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- optimagic.optimization.algo_options.STOPPING_MAXFUN = 1000000#
int: If the maximum number of function evaluation is reached, the optimization stops but we do not count this as successful convergence. The function evaluations used to evaluate a numerical gradient do not count for this.
- optimagic.optimization.algo_options.STOPPING_MAXFUN_GLOBAL = 1000#
int: If the maximum number of function evaluation is reached, the optimization stops but we do not count this as successful convergence. The function evaluations used to evaluate a numerical gradient do not count for this. Set to a lower number than STOPPING_MAX_CRITERION_EVALUATIONS for global optimizers.
- optimagic.optimization.algo_options.STOPPING_MAXITER = 1000000#
int: If the maximum number of iterations is reached, the optimization stops, but we do not count this as successful convergence. The difference to
max_criterion_evaluations
is that one iteration might need several criterion evaluations, for example in a line search or to determine if the trust region radius has to be shrunk.
- optimagic.optimization.algo_options.CONVERGENCE_SECOND_BEST_FTOL_ABS = 1e-08#
absolute criterion tolerance optimagic requires if no other stopping criterion apart from max iterations etc. is available this is taken from scipy (SLSQP’s value, smaller than Nelder-Mead).
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- optimagic.optimization.algo_options.CONVERGENCE_SECOND_BEST_XTOL_ABS = 1e-08#
The absolute parameter tolerance optimagic requires if no other stopping criterion apart from max iterations etc. is available. This is taken from pybobyqa.
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- optimagic.optimization.algo_options.MAX_LINE_SEARCH_STEPS = 20#
Inspired by scipy L-BFGS-B.
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- optimagic.optimization.algo_options.LIMITED_MEMORY_STORAGE_LENGTH = 10#
Taken from scipy L-BFGS-B.
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- optimagic.optimization.algo_options.CONSTRAINTS_ABSOLUTE_TOLERANCE = 1e-05#
Allowed tolerance of the equality and inequality constraints for values to be considered ‘feasible’.
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