iwopy.interfaces.pygmo.problem.UDP

class iwopy.interfaces.pygmo.problem.UDP[source]

Bases: object

Generic Problem to Pygmo UserDefinedProblem adapter

Parameters:

  • problem (iwopy.Problem) – The problem to optimize

  • c_tol (float) – Constraint tolerance

  • pop (bool) – Vectorized fitness computation

  • verbosity (int) – The verbosity level, 0 = silent

problem

The problem to optimize

Type:

iwopy.Problem

n_vars_all

The sum of int and float variable counts

Type:

int

n_fitness

The sum of objective and constraint counts

Type:

int

c_tol

Constraint tolerances

Type:

list of float

values

The function values, shape: (n_fitness,)

Type:

numpy.ndarray

pop

Vectorized fitness computation

Type:

bool

verbosity

The verbosity level, 0 = silent

Type:

int

__init__(problem, pop=False, verbosity=0)[source]

Methods

__init__(problem[, pop, verbosity])

batch_fitness(dvs)

finalize(pygmo_pop[, verbosity])

Finalize the problem.

fitness(dv)

get_bounds()

get_extra_info()

get_name()

get_nec()

get_nic()

get_nix()

get_nobj()

gradient(x)

gradient_sparsity()

has_batch_fitness()

has_gradient()

has_gradient_sparsity()

has_hessians()

has_hessians_sparsity()

has_set_seed()

__init__(problem, pop=False, verbosity=0)[source]
finalize(pygmo_pop, verbosity=1)[source]

Finalize the problem.

Parameters:

  • pygmo_pop (pygmo.Population) – The results from the solver

  • verbosity (int) – The verbosity level, 0 = silent

Returns:

results – The optimization results object

Return type:

iwopy.SingleObjOptResults