Configurations for algorithms, trackers, endgames, etc¶

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Tracking configs¶

class pybertini.tracking.config.SteppingConfig((object)arg1) → None¶

Bases: Boost.Python.instance

consecutive_successful_steps_before_stepsize_increase¶: This number of successful steps have to taken consecutively, and then the stepsize is permitted to increase

frequency_of_CN_estimation¶: How frequently the condition number should be updated. Less frequently is faster (estimation requires an additional linear solve), but may cause precision adjustment to lag behind.

initial_step_size¶: The initial stepsize when tracking is started. See also tracking.AMPTracker.reinitialize_initial_step_size

max_num_steps¶: The maximum number of steps. Tracking will die if it tries to take more than this number, sad day.

max_step_size¶: The maximum allowed stepsize during tracking. See also min_num_steps

min_num_steps¶: The minimum number of steps the tracker can take between now and then. This is useful if you are tracking closely between times, and want to guarantee some number of steps are taken. Then again, this could be wasteful, too.

min_step_size¶: The minimum stepsize the tracker is allowed to take. See also max_step_size

step_size_fail_factor¶: The scale factor for stepsize, after a fail happens. See also step_size_success_factor

step_size_success_factor¶: The scale factor for stepsize, after some consecutive steps. See also consecutive_successful_steps_before_stepsize_increase

class pybertini.tracking.config.NewtonConfig((object)arg1) → None¶

Bases: Boost.Python.instance

max_num_newton_iterations¶

min_num_newton_iterations¶

class pybertini.tracking.config.AMPConfig((object)arg1) → None¶

Bases: Boost.Python.instance

__init__( (object)arg1, (System)arg2) -> None

coefficient_bound¶

consecutive_successful_steps_before_precision_decrease¶

degree_bound¶

epsilon¶

max_num_precision_decreases¶

maximum_precision¶

phi¶

psi¶

safety_digits_1¶

safety_digits_2¶

set_amp_config_from((AMPConfig)arg1, (System)arg2) → None¶

set_bounds_and_epsilon_from((AMPConfig)arg1, (System)arg2) → None¶

set_phi_psi_from_bounds((AMPConfig)arg1) → None¶

class pybertini.tracking.config.FixedPrecisionConfig((object)arg1, (System)arg2) → None¶: Bases: Boost.Python.instance

Endgame configs¶

class pybertini.endgame.config.Endgame((object)arg1) → None¶

Bases: Boost.Python.instance

Generic endgame settings. Number of sample points, etc. Note that some of its configs are rational numbers

final_tolerance¶: The tolerance to which to track the path, using the endgame. Endgames require two consecutive estimates to be this close to each other under the relative infinity norm. Default value is 1e-11.

max_num_newton_iterations¶: the maximum number of newton iterations to be taken during sample point sharpening. Increasing this can help speed convergence, at the risk of path jumping.

min_track_time¶: The minimum distance from the target time to track to. Decreasing this may help failing runs succeed, or maybe not, because you are, after all, tracking toward a singularity.

num_sample_points¶: The number of points to use for extrapolant calculation. In the Power Series Endgame, the is the number of geometrically spaces points on the path. For Cauchy, this is the number of points on each circle tracked around the target time value.

sample_factor¶: The factor by which to space the geometrically spaced `distance’ between sample points, or sample circles for Cauchy.

sample_point_refinement_factor¶: Extra amount of tolerance for refining before computing the final approximation, during endgame.

Algorithm configs¶