RCAIDE.Framework.Optimization.Packages.additive.additive_setup

additive_setup

Classes

Additive_Solver()

class Additive_Solver

Bases: object

__init__()

Additive_Solve(problem, num_fidelity_levels=2, num_samples=10, max_iterations=10, tolerance=1e-06, opt_type='basic', num_starts=3, print_output=True)

Solves a multifidelity problem using an additive corrections

Assumptions: N/A

Source: N/A

Inputs: problem [nexus()] num_fidelity_levels [int] num_samples [int] max_iterations [int] tolerance [float] opt_type [str] num_starts [int] print_output [bool]

Outputs: (fOpt,xOpt) [tuple]

Properties Used: N/A

evaluate_model(problem, x, cons)

Solves the optimization problem to get the objective and constraints

Assumptions: N/A

Source: N/A

Inputs: problem [nexus()] x [array] cons [array]

Outputs: f [float] g [array]

Properties Used: N/A

evaluate_corrected_model(x, problem=None, obj_surrogate=None, cons_surrogate=None)

Evaluates the corrected model with the low fidelity plus the corrections

Assumptions: N/A

Source: N/A

Inputs: x [array] problem [nexus()] obj_surrogate [fun()] cons_surrogate [fun()]

Outputs: obj [float] const [array] fail [bool]

Properties Used: N/A

evaluate_expected_improvement(x, problem=None, obj_surrogate=None, cons_surrogate=None, fstar=inf, cons=None)

Evaluates the expected improvement of the point x

Assumptions: N/A

Source: N/A

Inputs: x [array] problem [nexus()] obj_surrogate [fun()] cons_surrogate [fun()] fstar [float] cons [vector]

Outputs: -EI [float] const [array] fail [bool]

Properties Used: N/A

scale_vals(inp, con, ini, bnd, scl)

Scales values to help setup the problem

Assumptions: N/A

Source: N/A

Inputs: inp [array] con [array] ini [array] bnd [array] scl [array]

Outputs:

tuple:

x [array] scaled_constraints [array] x_low_bounds [array] x_up_bounds [array] con_up_edge [array] con_low_edge [array]

Properties Used: N/A

initialize_opt_vals(opt_prob, obj, inp, x_low_bound, x_up_bound, con_low_edge, con_up_edge, nam, con, x_eval)

Sets up the optimization values

Assumptions: N/A

Source: N/A

Inputs: opt_prob [pyopt_problem()] obj [float] inp [array] x_low_bound [array] x_up_bound [array] con_low_edge [array] con_up_edge [array] nam [list of str] con [array] x_eval [array]

Outputs: N/A

Properties Used: N/A

unpack_constraints_slsqp(x, con_ind, sign, edge, problem, cons_surrogate)

initialize_opt_vals_SLSQP(obj, inp, x_low_bound, x_up_bound, con_low_edge, con_up_edge, nam, con, x_eval, problem, cons_surr)

initialize_opt_vals_SHGO(obj, inp, x_low_bound, x_up_bound, con_low_edge, con_up_edge, nam, con, problem, cons_surr)

run_objective_optimization(opt_prob, problem, f_additive_surrogate, g_additive_surrogate)

Runs SNOPT to optimize

Assumptions: N/A

Source: N/A

Inputs: opt_prob [pyopt_problem()] problem [nexus()] f_additive_surrogate [fun()] g_additive_surrogate [fun()]

Outputs: fOpt [float] xOpt [array]

Properties Used: N/A