Constants

This module provides a central place to define native python enums and constants that are used in multiple other modules

Classes Summary

ExitFlag(value)	Defines possible exitflag values for the optimizer to indicate why optimization exited.
Options(value)	Defines all the fields that can be specified in Options to Optimizer
StepBackStrategy(value)	Defines the possible choices of search refinement if proposed step reaches optimization boundary
SubSpaceDim(value)	Defines the possible choices of subspace dimension in which the subproblem will be solved.

Functions Summary

validate_options(options)

Check if the chosen options are valid

Classes

class fides.constants.ExitFlag(value)

Defines possible exitflag values for the optimizer to indicate why optimization exited. Negative value indicate errors while positive values indicate convergence.

DELTA_TOO_SMALL = -5

Trust Region Radius too small to proceed

DID_NOT_RUN = 0

Optimizer did not run

EXCEEDED_BOUNDARY = -4

Exceeded specified boundaries

FTOL = 1

Converged according to fval difference

GTOL = 3

Converged according to gradient norm

MAXITER = -1

Reached maximum number of allowed iterations

MAXTIME = -2

Expected to reach maximum allowed time in next iteration

NOT_FINITE = -3

Encountered non-finite fval/grad/hess

XTOL = 2

Converged according to x difference

class fides.constants.Options(value)

Defines all the fields that can be specified in Options to Optimizer

DELTA_INIT = 'delta_init'

initial trust region radius

ETA = 'eta'

trust region increase threshold for trust region ratio

FATOL = 'fatol'

absolute tolerance for convergence based on fval

FRTOL = 'frtol'

relative tolerance for convergence based on fval

GAMMA1 = 'gamma1'

factor by which trust region radius will be decreased

GAMMA2 = 'gamma2'

factor by which trust region radius will be increased

GATOL = 'gatol'

absolute tolerance for convergence based on grad

GRTOL = 'grtol'

relative tolerance for convergence based on grad

HISTORY_FILE = 'history_file'

when set, statistics for each start will

MAXITER = 'maxiter'

maximum number of allowed iterations

MAXTIME = 'maxtime'

maximum amount of walltime in seconds

MU = 'mu'

acceptance threshold for trust region ratio

STEPBACK_STRAT = 'stepback_strategy'

method to use for stepback

SUBSPACE_DIM = 'subspace_solver'

trust region subproblem subspace

THETA_MAX = 'theta_max'

maximal fraction of step that would hit bounds

XTOL = 'xtol'

tolerance for convergence based on x

class fides.constants.StepBackStrategy(value)

Defines the possible choices of search refinement if proposed step reaches optimization boundary

MIXED = 'mixed'

mix reflections and truncations

REFINE = 'refine'

perform optimization to refine step

REFLECT = 'reflect'

recursive reflections at boundary

SINGLE_REFLECT = 'reflect_single'

single reflection at boundary

TRUNCATE = 'truncate'

truncate step at boundary and re-solve

class fides.constants.SubSpaceDim(value)

Defines the possible choices of subspace dimension in which the subproblem will be solved.

FULL = 'full'

Full \(\mathbb{R}^n\)

STEIHAUG = 'scg'

CG subspace via Steihaug’s method

TWO = '2D'

Two dimensional Newton/Gradient subspace

Functions

fides.constants.validate_options(options)

Check if the chosen options are valid