smt_optim.benchmarks.misc package

Contents

smt_optim.benchmarks.misc package#

Submodules#

smt_optim.benchmarks.misc.avt module#

smt_optim.benchmarks.misc.edge_cases module#

class Rosenbrock2[source]#

Bases: BenchmarkProblem

bounds: ndarray = None#
constraints: list = None#
hf_constraint(x)[source]#
hf_constraint2(x)[source]#
hf_objective(x)[source]#
name: str = None#
num_cstr: int = None#
num_dim: int | str = None#
num_fidelity: int = None#
num_obj: int = None#
objective: Callable | list[Callable] = None#
set_dim(dim)#
tags: list = None#
class TwoConstraints[source]#

Bases: BenchmarkProblem

bounds: ndarray = None#
constraints: list = None#
cstr1(x)[source]#
cstr2(x)[source]#
func(x)[source]#
name: str = None#
num_cstr: int = None#
num_dim: int | str = None#
num_fidelity: int = None#
num_obj: int = None#
objective: Callable | list[Callable] = None#
set_dim(dim)#
tags: list = None#

smt_optim.benchmarks.misc.gano module#

class Gano[source]#

Bases: BenchmarkProblem

bounds: ndarray = None#
constraints: list = None#
gano_2a_f(x: ndarray) → ndarray[source]#
gano_2a_g(x: ndarray) → ndarray[source]#
gano_2b_f(x: ndarray) → ndarray[source]#
gano_2b_g(x: ndarray) → ndarray[source]#
name: str = None#
num_cstr: int = None#
num_dim: int | str = None#
num_fidelity: int = None#
num_obj: int = None#
objective: Callable | list[Callable] = None#
set_dim(dim)#
tags: list = None#
class MFG08[source]#

Bases: BenchmarkProblem

G8a_f(x: ndarray) → ndarray[source]#
G8a_g1(x: ndarray) → ndarray[source]#
G8a_g2(x: ndarray) → ndarray[source]#
G8b_f(x: ndarray) → ndarray[source]#
G8b_g1(x: ndarray) → ndarray[source]#
G8b_g2(x: ndarray) → ndarray[source]#
bounds: ndarray = None#
constraints: list = None#
name: str = None#
num_cstr: int = None#
num_dim: int | str = None#
num_fidelity: int = None#
num_obj: int = None#
objective: Callable | list[Callable] = None#
set_dim(dim)#
tags: list = None#

smt_optim.benchmarks.misc.mf_borehole module#

class MFBorehole[source]#

Bases: BenchmarkProblem

Reference:

(with 2 fidelity levels) Xiong, S., Qian, P. Z., & Wu, C. J. (2013). Sequential design and analysis of high-accuracy and low-accuracy computer codes. Technometrics, 55(1), 37-46.

(with 3 fidelity levels) Tran, A., Wildey, T., & McCann, S. (2020). sMF-BO-2CoGP: A sequential multi-fidelity constrained Bayesian optimization framework for design applications. Journal of Computing and Information Science in Engineering, 20(3), 031007.

bounds: ndarray = None#
constraints: list = None#
func(x, A=6.283185307179586, B=1.0)[source]#
name: str = None#
num_cstr: int = None#
num_dim: int | str = None#
num_fidelity: int = None#
num_obj: int = None#
objective: Callable | list[Callable] = None#
set_dim(dim)#
set_num_fid(num_fidelity)[source]#
tags: list = None#

smt_optim.benchmarks.misc.mf_colville module#

class MFColville[source]#

Bases: BenchmarkProblem

Reference:

(with 2 fidelity levels)

Song, X., Lv, L., Sun, W., & Zhang, J. (2019). A radial basis function-based multi-fidelity surrogate model: exploring correlation between high-fidelity and low-fidelity models. Structural & Multidisciplinary Optimization, 60(3), 965.

Note: (page 9) the term (x_3^2 - x_4) should be squared

A in [0, 1] controls the correlation between the lf and hf function. A=0.0 -> corr=0.0882 A=0.2 -> corr=0.1416 A=0.4 -> corr=0.6978 A=0.6 -> corr=0.9521 A=0.8 -> corr=0.9948 A=1.0 -> corr=1.0000

bounds: ndarray = None#
constraints: list = None#
func(x)[source]#
func_lf(x, A)[source]#
name: str = None#
num_cstr: int = None#
num_dim: int | str = None#
num_fidelity: int = None#
num_obj: int = None#
objective: Callable | list[Callable] = None#
set_dim(dim)#
tags: list = None#

smt_optim.benchmarks.misc.misc2 module#

class MFConstraintRosenbrock[source]#

Bases: BenchmarkProblem

Fischer, C. C. (2021). Bayesian Inspired Multi-Fidelity Optimization with Aerodynamic Design.

bounds: ndarray = None#
constraints: list = None#
g1(x)[source]#
g1_lf(x)[source]#
name: str = None#
num_cstr: int = None#
num_dim: int | str = None#
num_fidelity: int = None#
num_obj: int = None#
objective: Callable | list[Callable] = None#
set_dim(dim)[source]#
tags: list = None#

smt_optim.benchmarks.misc.mixvar_branin module#

Reference: Efficient global optimization of constrained mixed variable problems

class MixVarBranin[source]#

Bases: BenchmarkProblem

bounds: ndarray = None#
constraint(x)[source]#
constraints: list = None#
h(x)[source]#
name: str = None#
num_cstr: int = None#
num_dim: int | str = None#
num_fidelity: int = None#
num_obj: int = None#
objective(x)[source]#
set_dim(dim)#
tags: list = None#
class MixVarGoldstein[source]#

Bases: BenchmarkProblem

bounds: ndarray = None#
constraint(x)[source]#
constraints: list = None#
h(x)[source]#
name: str = None#
num_cstr: int = None#
num_dim: int | str = None#
num_fidelity: int = None#
num_obj: int = None#
objective(x)[source]#
set_dim(dim)#
tags: list = None#
class MultiFidelityMixVarBranin[source]#

Bases: BenchmarkProblem

bounds: ndarray = None#
constraint_lf(x)[source]#
constraints: list = None#
name: str = None#
num_cstr: int = None#
num_dim: int | str = None#
num_fidelity: int = None#
num_obj: int = None#
objective: Callable | list[Callable] = None#
objective_lf(x)[source]#
set_dim(dim)#
tags: list = None#

smt_optim.benchmarks.misc.original module#

class Branin1[source]#

Bases: BenchmarkProblem

[1]

bounds: ndarray = None#
constraints: list = None#
hf_constraint(x)[source]#
hf_objective(x)[source]#
lf_constraint(x)[source]#
lf_objective(x)[source]#
name: str = None#
num_cstr: int = None#
num_dim: int | str = None#
num_fidelity: int = None#
num_obj: int = None#
objective: Callable | list[Callable] = None#
set_dim(dim)#
tags: list = None#
class Branin2[source]#

Bases: BenchmarkProblem

[1]

f_min = 12.001 f_min_x = np.array([0.941, 0.317])

bounds: ndarray = None#
constraints: list = None#
hf_constraint(x)[source]#
hf_objective(x)[source]#
lf_constraint(x)[source]#
lf_objective(x)[source]#
name: str = None#
num_cstr: int = None#
num_dim: int | str = None#
num_fidelity: int = None#
num_obj: int = None#
objective: Callable | list[Callable] = None#
set_dim(dim)#
tags: list = None#
class BraninMF[source]#

Bases: BenchmarkProblem

bounds: ndarray = None#
constraints: list = None#
decompose_x(x)[source]#
hf_constraint(x)[source]#
hf_objective(x)[source]#
lf_constraint(x)[source]#
lf_objective(x)[source]#
name: str = None#
num_cstr: int = None#
num_dim: int | str = None#
num_fidelity: int = None#
num_obj: int = None#
objective: Callable | list[Callable] = None#
set_dim(dim)#
tags: list = None#
class Rosenbrock[source]#

Bases: BenchmarkProblem

bounds: ndarray = None#
constraints: list = None#
hf_constraint(x)[source]#
hf_objective(x)[source]#
lf_constraint(x)[source]#
lf_objective(x)[source]#
name: str = None#
num_cstr: int = None#
num_dim: int | str = None#
num_fidelity: int = None#
num_obj: int = None#
objective: Callable | list[Callable] = None#
set_dim(dim)#
tags: list = None#
class Sasena1[source]#

Bases: BenchmarkProblem

[1]

f_min = -1.1723 f_min_x = np.array([2.7450, 2.3523])

bounds: ndarray = None#
constraints: list = None#
hf_constraint(x)[source]#
hf_objective(x)[source]#
lf_constraint(x)[source]#
lf_objective(x)[source]#
name: str = None#
num_cstr: int = None#
num_dim: int | str = None#
num_fidelity: int = None#
num_obj: int = None#
objective: Callable | list[Callable] = None#
set_dim(dim)#
tags: list = None#
branin_forrester(x)[source]#

smt_optim.benchmarks.misc.weldedbeam_variants module#

class MFWeldedBeamDesign[source]#

Bases: BenchmarkProblem

References:

Datta, D., & Figueira, J. R. (2011). A real-integer-discrete-coded particle swarm optimization for design problems. Applied Soft Computing, 11(4), 3625-3633.

Tran, A., Wildey, T., & McCann, S. (2020). sMF-BO-2CoGP: A sequential multi-fidelity constrained Bayesian optimization framework for design applications. Journal of Computing and Information Science in Engineering, 20(3), 031007.

bounds: ndarray = None#
constraints: list = None#
expand_input(x, w, m, func)[source]#
name: str = None#
num_cstr: int = None#
num_dim: int = None#
num_fidelity: int = None#
num_obj: int = None#
objective: Callable | list[Callable] = None#
set_dim(dim)#
tags: list = None#
class MixedVarWeldedBeamDesign[source]#

Bases: BenchmarkProblem

References: Datta, D., & Figueira, J. R. (2011). A real-integer-discrete-coded particle swarm optimization for design problems. Applied Soft Computing, 11(4), 3625-3633. - Tran, A., Wildey, T., & McCann, S. (2020). sMF-BO-2CoGP: A sequential multi-fidelity constrained Bayesian optimization framework for design applications. Journal of Computing and Information Science in Engineering, 20(3), 031007.

P_c(x)[source]#
bending_stress(x)[source]#
bounds: ndarray = None#
buckling(x)[source]#
constraints: list = None#
deflection(x)[source]#
delta(x)[source]#
name: str = None#
num_cstr: int = None#
num_dim: int = None#
num_fidelity: int = None#
num_obj: int = None#
objective(x)[source]#
set_dim(dim)#
shear_stress(x)[source]#
side_constraints(x)[source]#
sigma(x)[source]#
tags: list = None#
tau(x)[source]#
weld_properties(x)[source]#

Module contents#

Contents