Egor (EGObox)

Egor is a gradient-free global optimizer from the EGObox library. As a bayesian optimizer, it is used to optimize expensive-to-evaluate black-box functions.

The modOpt Egor wrapper supports continuous design variables with finite bounds and nonlinear constraints with all standard bound forms:

• upper-bounded constraints c(x) <= u

• lower-bounded constraints c(x) >= l

• equality constraints c(x) = v

• double-sided constraints l <= c(x) <= u

Warning

Egor in modOpt currently requires finite lower and upper bounds on every design variable. Problems with unbounded variables are rejected.

Before using Egor, install egobox:

pip install egobox

or install via modOpt extras:

pip install "modopt[egobox]"

To use the Egor solver in modOpt, import it as:

from modopt import Egor

Then instantiate it with optional solver_options:

optimizer = Egor(prob, solver_options={"max_iters": 50, "n_doe": 10, "seed": 42})

modOpt forwards Egor options through two paths:

• constructor options are passed to egobox.Egor(...)

• runtime options are passed to Egor.minimize(...)

Note

For constrained problems, do not pass solver_options['cstr_specs'] directly. The modOpt wrapper builds cstr_specs automatically from cl and cu.

Egor solver options in modOpt

Option	Type (default value)	Description
max_iters	int (20)	Maximum number of Egor iterations. Passed to minimize().
gp_config	egobox.GpConfig	GP configuration used by the optimizer, see GpConfig for details.
n_start	int (20)	Number of runs of infill strategy optimizations; the best result is taken.
n_doe	int (0)	Number of samples of initial LHS sampling, used when DOE is not provided by the user. When 0, the number of points is computed automatically regarding the number of input variables of the function under optimization.
doe	None, list, tuple, or ndarray (None)	Initial DOE containing ns samples. Either nt = nx then only x is specified and ns evaluations are done to get y_doe values, or nt = nx + ny then x = doe[:, :nx] and y = doe[:, nx:] are provided.
infill_strategy	egobox.InfillStrategy (LOG_EI)	Infill criterion used to decide the next promising point.
cstr_infill	bool (False)	Activates the constrained infill criterion, where the product of probability of feasibility is used as a factor of the infill criterion.
cstr_strategy	egobox.ConstraintStrategy (MC)	Constraint management strategy for infill; use the mean value or the upper trusted bound.
qei_config	egobox.QEiConfig	Configuration for parallel qEI, also known as batch or multipoint evaluation. q points are selected at each iteration of the EGO algorithm.
infill_optimizer	egobox.InfillOptimizer (COBYLA)	Internal optimizer used to optimize the infill criterion; either COBYLA or SLSQP.
trego	object (None)	TREGO configuration to activate TREGO strategy for global optimization.
coego_n_coop	int (0)	Number of cooperative component groups used by the CoEGO algorithm.
target	float (-max_float)	Known optimum used as a stopping criterion.
failsafe_strategy	egobox.FailsafeStrategy (REJECTION)	Strategy to handle objective computation failure.
seed	int or None (None)	Random generator seed to allow computation reproducibility.
outdir	str or None (None)	Directory to write optimization history and use as a search path for warm-start DOE.
warm_start	bool (False)	Start by loading initial DOE from outdir.
hot_start	int or None (None)	When True, hot_start behaves like hot_start = 0 with no iteration extension. When hot_start >= 0, the optimizer state is saved and optimization restarts from a previous checkpoint.
run_info	object or None (None)	Optional run information object used for logging and saving results.
timeout	float, int, or None (None)	Optional timeout in seconds. The optimization stops when the elapsed time exceeds this duration.
verbose	int, egobox.Verbosity, or None (None)	Logging verbosity level. Default is None, which means Verbose.ERROR and possible control by the EGOBOX_LOG environment variable.
cstr_tol	None, list, tuple, or ndarray (None)	List of tolerances for constraints to be satisfied (cstr < tol).
cstr_specs	None, list, or tuple (None)	Optional list of CstrSpec objects describing how each surrogate-modeled constraint should be interpreted.
fcstrs	list or tuple ([])	List of constraints defined as functions.
fcstr_specs	list or tuple ([])	Optional list of CstrSpec objects, one per function constraint.

Note

Detailed information on egobox objects can be retrieved using the python interpreter. See example below.

> python
>>> help(egobox.GpConfig)