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Surrogate Library

The SGTELIB library is a dynamic surrogate modelling library. It is used in the Search step of Mads to dynamically construct models from the previous evaluations. During a Search step that uses SGTELIB, models of the objective and the constraints are constructed and a surrogate subproblem involving these models is optimized. The resulting solutions are the next candidates for evaluation by the true problem.

Models from the SGTELIB library can be used by setting the parameter SGTELIB_MODEL_SEARCH to yes or true.

Models

Models in SGTELIB are defined by using a succession of field names and field values. To choose a model, the parameter SGTELIB_MODEL_DEFINITION must be used followed by the field name TYPE, and then by the model type. The subsequent fields enable to define the settings of the model. Each field name is made of one single word and each field value is made of one single word or numerical value.

Example : SGTELIB_MODEL_DEFINITION TYPE <model type> FIELD1 <field 1 value> FIELD2 <field 2 value>

The section below describes the models and settings available.

Types of models

Below is the list of all possible models and their authorized fields.

PRS

PRS (Polynomial Response Surface) is a type of model.
Authorized fields:
Examples:
TYPE PRS DEGREE 2
TYPE PRS DEGREE OPTIM RIDGE OPTIM

PRS_EDGE

PRS_EDGE (Polynomial Response Surface EDGE) is a type of model that allows to model discontinuities at 0 by using additional basis functions.
Authorized fields:
Examples:
TYPE PRS_EDGE DEGREE 2
TYPE PRS_EDGE DEGREE OPTIM RIDGE OPTIM

PRS_CAT

PRS_CAT (Categorical Polynomial Response Surface) is a type of model that allows to build one PRS model for each different value of the first component of x.
Authorized fields:
Example:
TYPE PRS_CAT DEGREE 2
TYPE PRS_CAT DEGREE OPTIM RIDGE OPTIM

RBF

RBF (Radial Basis Function) is a type of model.
Authorized fields:
Example:
TYPE RBF KERNEL_TYPE D1 KERNEL_SHAPE OPTIM DISTANCE TYPE NORM2

KS

KS (Kernel Smoothing) is a type of model.
Authorized fields:
Example:
TYPE KS KERNEL_TYPE OPTIM KERNEL_SHAPE OPTIM

KRIGING

KRIGING is a type of model.
Authorized fields:
Example:
TYPE KRIGING

LOWESS

LOWESS (Locally Weighted Regression) is a type of model (from [TaAuKoLed2016]).
Authorized fields:
Example:
TYPE LOWESS DEGREE 1
TYPE LOWESS DEGREE OPTIM KERNEL_SHAPE OPTIM KERNEL_TYPE D1
TYPE LOWESS DEGREE OPTIM KERNEL_SHAPE OPTIM KERNEL_TYPE OPTIM DISTANCE TYPE OPTIM

CN

CN (Closest Neighbours) is a type of model.
Authorized fields:
Example:
TYPE CN

ENSEMBLE

ENSEMBLE is a type of model that uses multiple models simultaneously.
Authorized fields:
Example:
TYPE ENSEMBLE WEIGHT SELECT METRIC OECV
TYPE ENSEMBLE WEIGHT OPTIM METRIC RMSECV DISTANCE TYPE NORM2 BUDGET 100

ENSEMBLE_STAT

ENSEMBLE_STAT is a type of model (from [AuLedSa2021]).
Authorized fields:
  • all the fields from :ref:`ensemble` (with different default values though).
  • :ref:`uncertainty`: Selects an alternative for the uncertainty (smooth or nonsmooth).
  • :ref:`size_param`: Defines the size parameter (different meaning depending on the value of UNCERTAINTY).
  • :ref:`sigma_mult`: Defines the scaling factor of the uncertainty.
  • :ref:`lambda_p`: Defines the shape parameter of the probability of feasibility.
  • :ref:`lambda_pi`: Defines the shape parameter of the probability of improvement.
Example:
TYPE ENSEMBLE_STAT UNCERTAINTY SMOOTH WEIGHT SELECT5 METRIC RMSECV SIZE_PARAM 15

The following table summarizes the possible fields for every model.

Model authorized fields
Model type :ref:`degree` :ref:`ridge` :ref:`kernel_type` :ref:`kernel_shape` :ref:`distance_type` :ref:`preset` :ref:`weight` :ref:`metric` :ref:`uncertainty` :ref:`budget` :ref:`output`
:ref:`prs` ✔ ✔               ✔ ✔
:ref:`prs_edge` ✔ ✔               ✔ ✔
:ref:`prs_cat` ✔ ✔               ✔ ✔
:ref:`rbf`   ✔ ✔ ✔ ✔ ✔       ✔ ✔
:ref:`ks`     ✔ ✔ ✔         ✔ ✔
:ref:`kriging`   ✔     ✔         ✔ ✔
:ref:`lowess` ✔ ✔ ✔ ✔ ✔ ✔       ✔ ✔
:ref:`cn`         ✔         ✔ ✔
:ref:`ensemble`         ✔ ✔ ✔ ✔   ✔ ✔
:ref:`ensemble_stat`         ✔ ✔ ✔ ✔ ✔ ✔ ✔

Main model parameters

Below is the list of fields and their descriptions.

DEGREE

The field name DEGREE defines the degree of a polynomial response surface. The value must be an integer \geq 1.
Allowed for models of type: :ref:`prs`, :ref:`prs_edge`, :ref:`prs_cat` and :ref:`lowess`.
Default value: 5
  • For PRS models, the default degree is 2.
  • For LOWESS models, the degree must be 1 (default) or 2.
Example:
TYPE PRS DEGREE 3 defines a PRS model of degree 3.
TYPE PRS_EDGE DEGREE 2 defines a PRS_EDGE model of degree 2.
TYPE LOWESS DEGREE OPTIM defines a LOWESS model where the degree is optimized.

RIDGE

The field name RIDGE defines the regularization parameter of the model.
Possible values: Real value \geq 0. Recommended values are 0 and 0.001.
Default value: 0.001.
Example:
TYPE PRS DEGREE 3 RIDGE 0 defines a PRS model of degree 3 with no ridge.
TYPE PRS DEGREE OPTIM RIDGE OPTIM defines a PRS model where the degree and ridge coefficient are optimized.

KERNEL_TYPE

The field name KERNEL_TYPE defines the type of kernel used in the model. The field name KERNEL is equivalent.
Allowed for models of type: :ref:`rbf`, :ref:`lowess` and :ref:`ks`.
Possible values:
  • D1: Gaussian kernel
  • D2: Inverse Quadratic Kernel
  • D3: Inverse Multiquadratic Kernel
  • D4: Bi-quadratic Kernel
  • D5: Tri-cubic Kernel
  • D6: Exponential Sqrt Kernel
  • D7: Epanechnikov Kernel
  • I0: Multiquadratic Kernel
  • I1: Polyharmonic splines, degree 1
  • I2: Polyharmonic splines, degree 2
  • I3: Polyharmonic splines, degree 3
  • I4: Polyharmonic splines, degree 4
  • OPTIM: The type of kernel is optimized
Default value: D1, except for RBF models where it is I2.
Example:
TYPE KS KERNEL_TYPE D2 defines a KS model with Inverse Quadratic Kernel.
TYPE KS KERNEL_TYPE OPTIM KERNEL_SHAPE OPTIM defines a KS model with optimized kernel shape and type.

KERNEL_SHAPE

The field name KERNEL_SHAPE defines the shape coefficient of the kernel function. The field name KERNEL_COEF is equivalent. Note that this field name has no impact for kernel types I1, I2, I3 and I4 because these kernels do not include a shape parameter.
Allowed for models of type: :ref:`rbf`, :ref:`ks` and :ref:`lowess`.
Possible values: Real value \geq 0. Recommended range is [0.1; 10]. For KS and LOWESS model, small values lead to smoother models.
Default value: By default, the kernel coefficient is optimized.
Example:
TYPE RBF KERNEL_TYPE D4 KERNEL_SHAPE 10 defines a RBF model with an inverse bi-quadratic kernel of shape coefficient 10.
TYPE KS KERNEL_TYPE OPTIM KERNEL_SHAPE OPTIM defines a KS model with optimized kernel shape and type.

DISTANCE_TYPE

The field name DISTANCE_TYPE defines the distance function used in the model.
Possible values:
  • NORM1: Euclidian distance
  • NORM2: Distance based on norm 1
  • NORMINF: Distance based on norm 1
  • NORM2_IS0: Tailored distance for discontinuity in 0
  • NORM2_CAT: Tailored distance for categorical models
Default value: NORM2.
Example:
TYPE KS DISTANCE NORM2_IS0 defines a KS model tailored for VAN optimization.

PRESET

The field name PRESET defines the type of model used when applicable.
  • When applied to :ref:`rbf` models, PRESET defines the type of RBF.

    Possible values:

    • O: RBF with linear terms and orthogonal constraints
    • R: RBF with linear terms and regularization term
    • I: RBF with incomplete set of basis functions (see [AuKoLedTa2016] for RBFI models)

    Default value: I.
    Example:
    TYPE RBF PRESET O
  • When applied to :ref:`lowess` models [TaAuKoLed2016], PRESET defines how the weight w_i of each data point x_i is computed.

    Possible values:

    • D: w_i=\phi(d_i) where \phi is the kernel of type and shape defined by the fields :ref:`kernel_type` and :ref:`kernel_shape`, respectively, and d_i is the distance between the prediction point and the data point x_i
    • DEN: w_i=\phi(d_i/d_q) where d_q is the distance between the prediction point and the q^{th} closest data point, and d_q is computed with an empirical method
    • DGN: w_i=\phi(d_i/d_q) where d_q is computed with the Gamma method
    • RE: w_i=\phi(r_i) where r_i is the rank of x_i in terms of distance to the prediction point, and r_i is computed with empirical method
    • RG: w_i=\phi(r_i) where r_i is computed with the Gamma method
    • REN: same as RE but the ranks are normalized in [0,1]
    • RGN: same as RG but the ranks are normalized in [0,1]

    Default value: DGN.
    Example:
    TYPE LOWESS PRESET RE
  • When applied to :ref:`ensemble` or :ref:`ensemble_stat` models, PRESET determines the selection of models in the ensemble.

    Possible values:


    Default value: DEFAULT.
    Example:
    TYPE ENSEMBLE PRESET SUPER1

WEIGHT

The field name WEIGHT defines the method used to compute the weights \boldsymbol{w} of the ensemble of models. The field name WEIGHT_TYPE is equivalent.
Allowed for models of type: :ref:`ensemble` and :ref:`ensemble_stat`.
Possible values:
  • WTA1: w_k \propto \mathcal{E}_{sum} - \mathcal{E}_k
  • WTA3: w_k \propto (\mathcal{E}_k + \alpha\mathcal{E}_{mean})^{\beta}
  • SELECT: w_k \propto 1 if \mathcal{E}_k = \mathcal{E}_{min} (only the best model is selected)
  • SELECTN: w_k \propto \mathcal{E}_{sum}^N - \mathcal{E}_k (for N=1,2,\dots,6)
  • OPTIM: \boldsymbol{w} minimizes \mathcal{E}(\boldsymbol{w})

Where \mathcal{E}_k is the error metric (defined by the field name :ref:`metric`) of the k^{th} model in the ensemble, \mathcal{E}_{sum} is the cumulated error of all models, \mathcal{E}_{min} is the minimal error, \mathcal{E}_{mean} is the average error, \alpha=0.05, \beta=-1, and \mathcal{E}_{sum}^N is the cumulated error metric of the N best models.

Default value: SELECT for :ref:`ensemble` models, SELECT3 for :ref:`ensemble_stat` models with :ref:`uncertainty` set to SMOOTH, and SELECT4 for :ref:`ensemble_stat` models with :ref:`uncertainty` set to NONSMOOTH.
Example:
TYPE ENSEMBLE WEIGHT SELECT METRIC RMSECV defines an ensemble of models which selects the model that has the best RMSECV.
TYPE ENSEMBLE WEIGHT OPTIM METRIC RMSECV defines an ensemble of models where the weights \boldsymbol{w} are computed to minimize the RMSECV of the model.
TYPE ENSEMBLE WEIGHT SELECT3 METRIC OECV defines an ensemble of models which selects the 3 models that have the best OECV.

UNCERTAINTY

(specific to :ref:`ensemble_stat` models)

The field name UNCERTAINTY defines the type of uncertainty used in ENSEMBLE_STAT models.
Possible values:
  • SMOOTH: Smooth alternative of the uncertainty (default)
  • NONSMOOTH: Nonmooth alternative of the uncertainty
Example:
TYPE ENSEMBLE_STAT UNCERTAINTY NONSMOOTH

SIZE_PARAM

(advanced parameter specific to :ref:`ensemble_stat` models)

The field name SIZE_PARAM defines the size of the directions of either :
  • the simplex used to compute the simplex gradients of the models if the field :ref:`uncertainty` is set to SMOOTH
  • the positive spanning set used to compare models values if the field :ref:`uncertainty` is set to NONSMOOTH
Possible values: Real value \geq 0. Recommended range is [0.001; 0.1].
Default value: 0.001 if the field UNCERTAINTY is set to SMOOTH, 0.005 if the field UNCERTAINTY is set to NONSMOOTH.
Example:
TYPE ENSEMBLE_STAT UNCERTAINTY SMOOTH SIZE_PARAM 0.003

SIGMA_MULT

(advanced parameter specific to :ref:`ensemble_stat` models)

The field name SIGMA_MULT defines the scaling factor of the uncertain to be multiplied by the variance of already sampled function values.
Possible values: Real value \geq 0. Recommended range is [1; 100].
Default value: 10.
Example:
TYPE ENSEMBLE_STAT UNCERTAINTY NONSMOOTH SIGMA_MULT 30

LAMBDA_P

(advanced parameter specific to :ref:`ensemble_stat` models)

The field name LAMBDA_P defines the shape parameter of the probability of feasibility (P).
Possible values: Real value \geq 0. Recommended range is [0.1; 10].
Default value: 3 if the field UNCERTAINTY is set to SMOOTH, 1 if the field UNCERTAINTY is set to NONSMOOTH.
Example:
TYPE ENSEMBLE_STAT UNCERTAINTY NONSMOOTH LAMBDA_P 1.5

LAMBDA_PI

(advanced parameterspecific to :ref:`ensemble_stat` models)

The field name LAMBDA_PI defines the shape parameter of the probability of improvement (PI).
Possible values: Real value \geq 0. Recommended range is [0.01; 3].
Default value: 0.1 if the field UNCERTAINTY is set to SMOOTH, 0.5 if the field UNCERTAINTY is set to NONSMOOTH.
Example:
TYPE ENSEMBLE_STAT UNCERTAINTY NONSMOOTH LAMBDA_PI 0.3

OUTPUT

Defines a text file in which model information are recorded. Allowed for ALL types of model.

Parameter optimization and selection

Below is the list of some field names and values that influence the behaviour of other fields.

OPTIM

The field value OPTIM indicates that the model parameter must be optimized. The default optimization criteria is the AOECV error metric (except for ENSEMBLE_STAT models where it is OECV).
Parameters that can be optimized:
Example:
TYPE PRS DEGREE OPTIM
TYPE LOWESS DEGREE OPTIM KERNEL_TYPE OPTIM KERNEL_SHAPE OPTIM METRIC ARMSECV

METRIC

The field name METRIC defines the metric used to select the parameters of the model (including the weights of Ensemble models).
Allowed for ALL types of model.
Possible values:
  • EMAX: Error Max
  • EMAXCV: Error Max with Cross-Validation
  • RMSE: Root Mean Square Error
  • RMSECV: RMSE with Cross-Validation
  • OE: Order Error
  • OECV: Order Error with Cross-Validation [AuKoLedTa2016]
  • LINV: Invert of the Likelihood
  • AOE: Aggregate Order Error
  • AOECV: Aggregate Order Error with Cross-Validation [TaAuKoLed2016]
Default value: AOECV, except for :ref:`ensemble_stat` models where it is OECV.
Example:
TYPE ENSEMBLE WEIGHT SELECT METRIC RMSECV defines an ensemble of models which selects the model that has the best RMSECV.

BUDGET

Budget for model parameter optimization. The number of sets of model parameters that are tested is equal to the optimization budget multiplied by the number of parameters to optimize.
Allowed for ALL types of model.
Default value: 20
Example:
TYPE LOWESS KERNEL_SHAPE OPTIM METRIC AOECV BUDGET 100
TYPE ENSEMBLE WEIGHT OPTIM METRIC RMSECV BUDGET 50

Surrogate subproblem formulations

The SGTELIB library offers different formulations of the surrogate subproblem to be optimized at the Search step (see [TaLeDKo2014]). The SGTELIB_MODEL_FORMULATION parameter enables to choose a formulation, and the parameter SGTELIB_MODEL_DIVERSIFICATION enables to adjust a diversification parameter.

SGTELIB_MODEL_FORMULATION

The formulations of the surrogate subproblem involve various quantities.
\hat f denotes a model of the objective f and \hat c_j a model of the constraint c_j, j=1,2,\dots,m. For x\in X, \sigma_f(x) denotes the uncertainty associated with the prediction \hat f(x), and \sigma_j(x) denotes the uncertainty associated with the prediction \hat c_j(x), j=1,2,\dots,m. This uncertainty depends on the model chosen.
For a :ref:`kriging` model, \sigma_f(x) (or \sigma_j(x)) is readily available through the standard deviation that the model natively produces.
For an :ref:`ensemble_stat` model, the uncertainty is constructed by comparing the predictions of the ensemble models (see [AuLedSa2021]).
For any other model except ENSEMBLE, \sigma_f(x) (or \sigma_j(x)) is computed with the distance from x to previously evaluated points.
Finally, for an :ref:`ensemble` model, the uncertainty is computed through a weighted sum of the squared uncertainties of the ensemble models.
There are eight different formulations that can be chosen with the parameter SGTELIB_MODEL_FORMULATION. Some formulations involve a parameter \lambda that is described later.
  • FS (default):
\min_{x\in X}&\ \ \hat f(x)-\lambda\hat\sigma_f(x) \\
\mathrm{s.t.}&\ \ \hat c_j(x)-\lambda\hat\sigma_j(x)\leq0,\ \ j=1,2,\dots,m
  • FSP:
\min_{x\in X}&\ \ \hat f(x)-\lambda\hat\sigma_f(x) \\
\mathrm{s.t.}&\ \ \mathrm{P}(x)\geq 0.5

where \mathrm{P} is the probability of feasibility which is the probability that a given point is feasible.

  • EIS:
\min_{x\in X}&\ -\mathrm{EI}(x)-\lambda\hat\sigma_f(x) \\
\mathrm{s.t.}&\ \ \hat c_j(x)-\lambda\hat\sigma_j(x)\leq0,\ \ j=1,2,\dots,m

where \mathrm{EI} is the expected improvement that takes into account the probability of improvement and the expected amplitude thereof.

  • EFI:
\min_{x\in X}\ -\mathrm{EFI}(x)

where \mathrm{EFI} is the expected feasible improvement : \mathrm{EFI} = \mathrm{EI}\times\mathrm{P}.

  • EFIS:
\min_{x\in X}\ -\mathrm{EFI}(x)-\lambda\hat\sigma_f(x)
  • EFIM:
\min_{x\in X}\ -\mathrm{EFI}(x)-\lambda\hat\sigma_f(x)\mu(x)

where \mu is the uncertainty in the feasibility : \mu = 4\mathrm{P}\times(1-\mathrm{P}).

  • EFIC:
\min_{x\in X}\ -\mathrm{EFI}(x)-\lambda(\mathrm{EI}(x)\mu(x)
+\mathrm{P}(x)\hat\sigma_f(x))
  • PFI:
\min_{x\in X}\ -\mathrm{PFI}(x)

where \mathrm{PFI} is the probability of improvement : \mathrm{PFI} = \mathrm{PI}\times\mathrm{P}, with \mathrm{PI} being the probability of improvement which is the probability that the objective decreases from the best known value at a given point.

Example:
SGTELIB_MODEL_DEFINITION TYPE KRIGING
SGTELIB_MODEL_FORMULATION EFIC
The two lines above define a surrogate subproblem based on the EFIC formulation that will involve kriging models.

SGTELIB_MODEL_DIVERSIFICATION

The exploration parameter \lambda enables to control the exploration of the search space against the intensification in the most promising areas. A higher \lambda favors exploration whereas a lower \lambda favors intensification.
\lambda is a real value in [0,1] defined by the parameter SGTELIB_MODEL_DIVERSIFICATION.
Default value : 0.01.
Example:
SGTELIB_MODEL_DEFINITION TYPE ENSEMBLE
SGTELIB_MODEL_FORMULATION FSP
SGTELIB_MODEL_DIVERSIFICATION 0.1
The three lines above define a surrogate subproblem based on the FSP formulation with an exploration parameter equals to 0.1 that will involve ensemble models.

References

[TaAuKoLed2016](1, 2, 3) B.Talgorn, C.Audet, M.Kokkolaras and S.Le Digabel. Locally weighted regression models for surrogate-assisted design optimization. Optimization and Engineering, 19(1):213–238, 2018.
[TaLeDKo2014]B.Talgorn, S.Le Digabel and M.Kokkolaras. Statistical Surrogate Formulations for Simulation-Based Design Optimization. Journal of Mechanical Design, 137(2):021405–1–021405–18, 2015
[AuKoLedTa2016](1, 2) C.Audet, M.Kokkolaras, S.Le Digabel and B.Talgorn. Order-based error for managing ensembles of surrogates in mesh adaptive direct search Journal of Global Optimization, 70(3):645–675, 2018.
[AuLedSa2021](1, 2) C.Audet, S.Le Digabel and R.Saltet. Quantifying uncertainty with ensembles of surrogates for blackbox optimization. Rapport technique G-2021-37, Les cahiers du GERAD, 2021. http://www.optimization-online.org/DB_HTML/2021/07/8489.html