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.

Type

float

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.

Type

float

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.

Type

float

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.

Type

float

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.

Type

float

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.

Type

float

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.

Type

float

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).

Type

float

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.

Type

float

optimagic.optimization.algo_options.MAX_LINE_SEARCH_STEPS = 20

Inspired by scipy L-BFGS-B.

Type

int

optimagic.optimization.algo_options.LIMITED_MEMORY_STORAGE_LENGTH = 10

Taken from scipy L-BFGS-B.

Type

int

optimagic.optimization.algo_options.CONSTRAINTS_ABSOLUTE_TOLERANCE = 1e-05

Allowed tolerance of the equality and inequality constraints for values to be considered ‘feasible’.

Type

float

optimagic.optimization.algo_options.get_population_size(population_size, x, lower_bound=10)

Default population size for genetic algorithms.