A wrapper that provides finite distance differentiation on a regular grid for selected or all problem float variables.

Attributes¶

grid: iwopy.tools.RegularDiscretizationGrid

The discretization grid

order: dict

Finite difference order. Key: variable name str, value: 1 = forward, -1 = backward, 2 = centre

orderb: dict or int

Finite difference order of boundary points. Key: variable name str, value: order int

Public members¶

DiscretizeRegGrid(base_problem, deltas, fd_order=1, ...)[source]

Constructor

initialize(verbosity=1)[source]

Initialize the problem.

apply_individual(vars_int, vars_float)[source]

Apply new variables to the problem.

apply_population(vars_int, vars_float)[source]

Apply new variables to the problem, for a whole population.

evaluate_individual(vars_int, vars_float, ret_prob_res=False)[source]

Evaluate a single individual of the problem.

evaluate_population(vars_int, vars_float, ret_prob_res=False)[source]

Evaluate all individuals of a population.

finalize_individual(vars_int, vars_float, verbosity=1)[source]

Finalization, given the champion data.

finalize_population(vars_int, vars_float, verbosity=0)[source]

Finalization, given the final population data.

calc_gradients(vars_int, vars_float, func, components, ivars, ...)[source]

The actual gradient calculation, not to be called directly (call get_gradients instead).

var_names_int()[source]

The names of integer variables.

initial_values_int()[source]

The initial values of the integer variables.

min_values_int()[source]

The minimal values of the integer variables.

max_values_int()[source]

The maximal values of the integer variables.

var_names_float()[source]

The names of float variables.

initial_values_float()[source]

The initial values of the float variables.

min_values_float()[source]

The minimal values of the float variables.

max_values_float()[source]

The maximal values of the float variables.

INT_INF = -999999

property n_vars_int

The number of int variables

property n_vars_float

The number of float variables

add_objective(objective, varmap_int=None, varmap_float=None, ...)[source]

Add an objective to the problem.

add_constraint(constraint, varmap_int=None, varmap_float=None, ...)[source]

Add a constraint to the problem.

property min_values_constraints

Gets the minimal values of constraints

property max_values_constraints

Gets the maximal values of constraints

property constraints_tol

Gets the tolerance values of constraints

property n_objectives

The total number of objectives, i.e., the sum of all components

property n_constraints

The total number of constraints, i.e., the sum of all components

get_gradients(vars_int, vars_float, func=None, components=None, ...)[source]

Obtain gradients of a function that is linked to the problem.

property maximize_objs

Flags for objective maximization

check_constraints_individual(constraint_values, verbosity=0)[source]

Check if the constraints are fullfilled for the given individual.

check_constraints_population(constraint_values, verbosity=0)[source]

Check if the constraints are fullfilled for the given population.

prob_res_einsum_individual(prob_res_list, coeffs)[source]

Calculate the einsum of problem results

prob_res_einsum_population(prob_res_list, coeffs)[source]

Calculate the einsum of problem results

classmethod new(problem_type, *args, **kwargs)[source]

Run-time problem factory.

__str__()[source]

Get info string

property initialized

Flag for finished initialization

finalize(verbosity=0)[source]

Finalize the object.