rbfopt_test_functions module¶

Test functions.

This module implements several known mathematical functions, that can be used to test RBFOpt.

Licensed under Revised BSD license, see LICENSE. (C) Copyright Singapore University of Technology and Design 2014. (C) Copyright International Business Machines Corporation 2017.

class rbfopt_test_functions.TestBlackBox(name)[source]¶

Bases: RbfoptBlackBox

A black-box constructed from a known test function.

Parameters

namestring: The name of the function to be implemented.

evaluate(point)[source]¶

Evaluate the black-box function.

Parameters

x1D numpy.ndarray[float]: Value of the decision variables.

Returns

float: Value of the function at x.

evaluate_noisy(point)[source]¶

Evaluate a fast approximation of the black-box function.

Returns an approximation of the value of evaluate(), hopefully much more quickly, and provides error bounds on the evaluation. If has_evaluate_noisy() returns False, this function will never be queried and therefore it does not have to return any value.

Parameters

x1D numpy.ndarray[float]: Value of the decision variables.

Returns

1D numpy.ndarray[float]: A numpy array with three floats (value, lower, upper) containing the approximate value of the function at x, the lower error bound, and the upper error bound, such that the true function value is contained between value + lower and value + upper. Hence, lower should be <= 0 while upper should be >= 0.

get_dimension()[source]¶

Return the dimension of the problem.

Returns

int: The dimension of the problem.

get_var_lower()[source]¶

Return the array of lower bounds on the variables.

Returns

1D numpy.ndarray[float]: Lower bounds of the decision variables.

get_var_type()[source]¶

Return the type of each variable.

Returns

1D numpy.ndarray[char]: An array of length equal to dimension, specifying the type of each variable. Possible types are ‘R’ for real (continuous) variables, ‘I’ for integer (discrete) variables, ‘C’ for categorical (discrete, unordered). Bounds for categorical variables are interpreted the same way as for integer variables, but categorical variables are handled differently by the optimization algorithm; e.g., a categorical variable with bounds [2, 4] can take the value 2, 3 or 4.

get_var_upper()[source]¶

Return the array of upper bounds on the variables.

Returns

1D numpy.ndarray[float]: Upper bounds of the decision variables.

has_evaluate_noisy()[source]¶

Indicate whether evaluate_noisy is available.

Indicate if a fast but potentially noisy version of evaluate is available through the function evaluate_noisy. If True, such function will be used to try to accelerate convergence of the optimization algorithm. If False, the function evaluate_noisy will never be queried.

Returns

bool: Is evaluate_noisy available?

class rbfopt_test_functions.TestEnlargedBlackBox(name, dimension_multiplier=1)[source]¶

Bases: RbfoptBlackBox

A black-box constructed increasing the size of a test function.

Construct a black box function from a given function, increasing its dimension by a given factor. The new function is put together from several independent copies of the original function, plus a coupling term. If the dimension muldiplier is d and the original function has dimension n, the new function has dimension n*d and is computed as:

$\sum_{j=1}^{d} a_j f(x_{(j-1)n+1},\dots,x_{jn}) + 0.4 f(g_1(x),\dots,g_n(x)),$

where a_j are random weights that add up to 0.6, and g_1 through g_n are linear functions of a random subset of the variables. These linear function are appropriately scaled and clipped so that we do not exceed the original function bounds. The optimum of the new function stays the same. Finally, all variables are randomly permuted.

Parameters

namestring: The name of the function to be implemented.
dimension_multiplierint: Dimension multiplier

evaluate(point)[source]¶

Evaluate the black-box function.

Parameters

x1D numpy.ndarray[float]: Value of the decision variables.

Returns

float: Value of the function at x.

evaluate_noisy(point)[source]¶

Evaluate a fast approximation of the black-box function.

Returns an approximation of the value of evaluate(), hopefully much more quickly, and provides error bounds on the evaluation. If has_evaluate_noisy() returns False, this function will never be queried and therefore it does not have to return any value.

Parameters

x1D numpy.ndarray[float]: Value of the decision variables.

Returns

1D numpy.ndarray[float]: A numpy array with three floats (value, lower, upper) containing the approximate value of the function at x, the lower error bound, and the upper error bound, such that the true function value is contained between value + lower and value + upper. Hence, lower should be <= 0 while upper should be >= 0.

get_dimension()[source]¶

Return the dimension of the problem.

Returns

int: The dimension of the problem.

get_var_lower()[source]¶

Return the array of lower bounds on the variables.

Returns

1D numpy.ndarray[float]: Lower bounds of the decision variables.

get_var_type()[source]¶

Return the type of each variable.

Returns

1D numpy.ndarray[char]: An array of length equal to dimension, specifying the type of each variable. Possible types are ‘R’ for real (continuous) variables, ‘I’ for integer (discrete) variables, ‘C’ for categorical (discrete, unordered). Bounds for categorical variables are interpreted the same way as for integer variables, but categorical variables are handled differently by the optimization algorithm; e.g., a categorical variable with bounds [2, 4] can take the value 2, 3 or 4.

get_var_upper()[source]¶

Return the array of upper bounds on the variables.

Returns

1D numpy.ndarray[float]: Upper bounds of the decision variables.

has_evaluate_noisy()[source]¶

Indicate whether evaluate_noisy is available.

Indicate if a fast but potentially noisy version of evaluate is available through the function evaluate_noisy. If True, such function will be used to try to accelerate convergence of the optimization algorithm. If False, the function evaluate_noisy will never be queried.

Returns

bool: Is evaluate_noisy available?

class rbfopt_test_functions.TestNoisyBlackBox(blackbox, max_rel_error=0.1, max_abs_error=0.1)[source]¶

Bases: RbfoptBlackBox

A noisy black-box constructed from a given black-box function.

Parameters

blackboxRbfoptBlackBox: The black box function to which noise is added.
max_rel_error: float: Maximum relative error.
max_abs_error: float: Maximum absolute error.

evaluate(point)[source]¶

Evaluate the black-box function.

Parameters

x1D numpy.ndarray[float]: Value of the decision variables.

Returns

float: Value of the function at x.

evaluate_noisy(point)[source]¶

Evaluate a fast approximation of the black-box function.

Returns an approximation of the value of evaluate(), hopefully much more quickly, and provides error bounds on the evaluation. If has_evaluate_noisy() returns False, this function will never be queried and therefore it does not have to return any value.

Parameters

x1D numpy.ndarray[float]: Value of the decision variables.

Returns

1D numpy.ndarray[float]: A numpy array with three floats (value, lower, upper) containing the approximate value of the function at x, the lower error bound, and the upper error bound, such that the true function value is contained between value + lower and value + upper. Hence, lower should be <= 0 while upper should be >= 0.

get_dimension()[source]¶

Return the dimension of the problem.

Returns

int: The dimension of the problem.

get_var_lower()[source]¶

Return the array of lower bounds on the variables.

Returns

1D numpy.ndarray[float]: Lower bounds of the decision variables.

get_var_type()[source]¶

Return the type of each variable.

Returns

1D numpy.ndarray[char]: An array of length equal to dimension, specifying the type of each variable. Possible types are ‘R’ for real (continuous) variables, ‘I’ for integer (discrete) variables, ‘C’ for categorical (discrete, unordered). Bounds for categorical variables are interpreted the same way as for integer variables, but categorical variables are handled differently by the optimization algorithm; e.g., a categorical variable with bounds [2, 4] can take the value 2, 3 or 4.

get_var_upper()[source]¶

Return the array of upper bounds on the variables.

Returns

1D numpy.ndarray[float]: Upper bounds of the decision variables.

has_evaluate_noisy()[source]¶

Indicate whether evaluate_noisy is available.

Indicate if a fast but potentially noisy version of evaluate is available through the function evaluate_noisy. If True, such function will be used to try to accelerate convergence of the optimization algorithm. If False, the function evaluate_noisy will never be queried.

Returns

bool: Is evaluate_noisy available?

class rbfopt_test_functions.branin[source]¶

Bases: object

Branin function of the Dixon-Szego test set.

additional_optima = <Mock name='mock.array()' id='140550716838352'>¶

dimension = 2¶

classmethod evaluate(x)[source]¶

optimum_point = <Mock name='mock.array()' id='140550716838352'>¶

optimum_value = 0.397887357729739¶

var_lower = <Mock name='mock.array()' id='140550716838352'>¶

var_type = <Mock name='mock.array()' id='140550716838352'>¶

var_upper = <Mock name='mock.array()' id='140550716838352'>¶

class rbfopt_test_functions.branin_cat[source]¶

Bases: object

Branin function of the Dixon-Szego test set, with categorical vars.

additional_optima = <Mock name='mock.array()' id='140550716838352'>¶

dimension = 3¶

classmethod evaluate(x)[source]¶

optimum_point = <Mock name='mock.array()' id='140550716838352'>¶

optimum_value = 0.397887357729739¶

var_lower = <Mock name='mock.array()' id='140550716838352'>¶

var_type = <Mock name='mock.array()' id='140550716838352'>¶

var_upper = <Mock name='mock.array()' id='140550716838352'>¶

class rbfopt_test_functions.camel[source]¶

Bases: object

Six-hump Camel function of the Dixon-Szego test set.

dimension = 2¶

classmethod evaluate(x)[source]¶

optimum_point = <Mock name='mock.array()' id='140550716838352'>¶

optimum_value = -1.0316284535¶

var_lower = <Mock name='mock.array()' id='140550716838352'>¶

var_type = <Mock name='mock.array()' id='140550716838352'>¶

var_upper = <Mock name='mock.array()' id='140550716838352'>¶

class rbfopt_test_functions.ex4_1_1[source]¶

Bases: object

ex4_1_1 function of the GlobalLib test set.

dimension = 1¶

classmethod evaluate(x)[source]¶

optimum_point = <Mock name='mock.array()' id='140550716838352'>¶

optimum_value = -7.487312360731¶

var_lower = <Mock name='mock.array()' id='140550716838352'>¶

var_type = <Mock name='mock.array()' id='140550716838352'>¶

var_upper = <Mock name='mock.array()' id='140550716838352'>¶

class rbfopt_test_functions.ex4_1_2[source]¶

Bases: object

ex4_1_2 function of the GlobalLib test set.

dimension = 1¶

classmethod evaluate(x)[source]¶

optimum_point = <Mock name='mock.array()' id='140550716838352'>¶

optimum_value = -663.4993631230575¶

var_lower = <Mock name='mock.array()' id='140550716838352'>¶

var_type = <Mock name='mock.array()' id='140550716838352'>¶

var_upper = <Mock name='mock.array()' id='140550716838352'>¶

class rbfopt_test_functions.ex8_1_1[source]¶

Bases: object

ex8_1_1 function of the GlobalLib test set.

dimension = 2¶

classmethod evaluate(x)[source]¶

optimum_point = <Mock name='mock.array()' id='140550716838352'>¶

optimum_value = -2.0218067833¶

var_lower = <Mock name='mock.array()' id='140550716838352'>¶

var_type = <Mock name='mock.array()' id='140550716838352'>¶

var_upper = <Mock name='mock.array()' id='140550716838352'>¶

class rbfopt_test_functions.ex8_1_1_cat[source]¶

Bases: object

ex8_1_1 function of the GlobalLib test set.

dimension = 4¶

classmethod evaluate(x)[source]¶

optimum_point = <Mock name='mock.array()' id='140550716838352'>¶

optimum_value = -2.4161466378205514¶

var_lower = <Mock name='mock.array()' id='140550716838352'>¶

var_type = <Mock name='mock.array()' id='140550716838352'>¶

var_upper = <Mock name='mock.array()' id='140550716838352'>¶

class rbfopt_test_functions.ex8_1_4[source]¶

Bases: object

ex8_1_4 function of the GlobalLib test set.

dimension = 2¶

classmethod evaluate(x)[source]¶

optimum_point = <Mock name='mock.array()' id='140550716838352'>¶

optimum_value = 0.0¶

var_lower = <Mock name='mock.array()' id='140550716838352'>¶

var_type = <Mock name='mock.array()' id='140550716838352'>¶

var_upper = <Mock name='mock.array()' id='140550716838352'>¶

class rbfopt_test_functions.gear[source]¶

Bases: object

gear function of the MINLPLib test set.

dimension = 4¶

classmethod evaluate(x)[source]¶

optimum_point = <Mock name='mock.array()' id='140550716838352'>¶

optimum_value = 0.0¶

var_lower = <Mock name='mock.array()' id='140550716838352'>¶

var_type = <Mock name='mock.array()' id='140550716838352'>¶

var_upper = <Mock name='mock.array()' id='140550716838352'>¶

class rbfopt_test_functions.gear4[source]¶

Bases: object

gear4 function of the MINLPLib test set.

dimension = 5¶

classmethod evaluate(x)[source]¶

optimum_point = <Mock name='mock.array()' id='140550716838352'>¶

optimum_value = 1.6434284739¶

var_lower = <Mock name='mock.array()' id='140550716838352'>¶

var_type = <Mock name='mock.array()' id='140550716838352'>¶

var_upper = <Mock name='mock.array()' id='140550716838352'>¶

class rbfopt_test_functions.gear4_cat[source]¶

Bases: object

gear4 function of the MINLPLib test set, with categorical variables

dimension = 6¶

classmethod evaluate(x)[source]¶

optimum_point = <Mock name='mock.array()' id='140550716838352'>¶

optimum_value = 1.6434284739¶

var_lower = <Mock name='mock.array()' id='140550716838352'>¶

var_type = <Mock name='mock.array()' id='140550716838352'>¶

var_upper = <Mock name='mock.array()' id='140550716838352'>¶

class rbfopt_test_functions.goldsteinprice[source]¶

Bases: object

Goldstein & Price function of the Dixon-Szego test set.

dimension = 2¶

classmethod evaluate(x)[source]¶

optimum_point = <Mock name='mock.array()' id='140550716838352'>¶

optimum_value = 3¶

var_lower = <Mock name='mock.array()' id='140550716838352'>¶

var_type = <Mock name='mock.array()' id='140550716838352'>¶

var_upper = <Mock name='mock.array()' id='140550716838352'>¶

class rbfopt_test_functions.hartman3[source]¶

Bases: object

Hartman3 function of the Dixon-Szego test set.

a = [[3.0, 0.1, 3.0, 0.1], [10.0, 10.0, 10.0, 10.0], [30.0, 35.0, 30.0, 35.0]]¶

c = [1.0, 1.2, 3.0, 3.2]¶

dimension = 3¶

classmethod evaluate(x)[source]¶

optimum_point = <Mock name='mock.array()' id='140550716838352'>¶

optimum_value = -3.8626347486217725¶

p = [[0.3689, 0.4699, 0.1091, 0.03815], [0.117, 0.4387, 0.8732, 0.5743], [0.2673, 0.747, 0.5547, 0.8828]]¶

var_lower = <Mock name='mock.array()' id='140550716838352'>¶

var_type = <Mock name='mock.array()' id='140550716838352'>¶

var_upper = <Mock name='mock.array()' id='140550716838352'>¶

class rbfopt_test_functions.hartman3_cat[source]¶

Bases: object

Hartman3 function of the Dixon-Szego test set, with categorical vars.

a = <Mock name='mock.array()' id='140550716838352'>¶

c = <Mock name='mock.array()' id='140550716838352'>¶

dimension = 4¶

classmethod evaluate(x)[source]¶

optimum_point = <Mock name='mock.array()' id='140550716838352'>¶

optimum_value = -4.822787424687719¶

p = <Mock name='mock.array()' id='140550716838352'>¶

var_lower = <Mock name='mock.array()' id='140550716838352'>¶

var_type = <Mock name='mock.array()' id='140550716838352'>¶

var_upper = <Mock name='mock.array()' id='140550716838352'>¶

class rbfopt_test_functions.hartman6[source]¶

Bases: object

Hartman6 function of the Dixon-Szego test set.

a = [[10.0, 0.05, 3.0, 17.0], [3.0, 10.0, 3.5, 8.0], [17.0, 17.0, 1.7, 0.05], [3.5, 0.1, 10.0, 10.0], [1.7, 8.0, 17.0, 0.1], [8.0, 14.0, 8.0, 14.0]]¶

c = [1.0, 1.2, 3.0, 3.2]¶

dimension = 6¶

classmethod evaluate(x)[source]¶

optimum_point = <Mock name='mock.array()' id='140550716838352'>¶

optimum_value = -3.32236801141551¶

p = [[0.1312, 0.2329, 0.2348, 0.4047], [0.1696, 0.4135, 0.1451, 0.8828], [0.5569, 0.8307, 0.3522, 0.8732], [0.0124, 0.3736, 0.2883, 0.5743], [0.8283, 0.1004, 0.3047, 0.1091], [0.5886, 0.9991, 0.665, 0.0381]]¶

var_lower = <Mock name='mock.array()' id='140550716838352'>¶

var_type = <Mock name='mock.array()' id='140550716838352'>¶

var_upper = <Mock name='mock.array()' id='140550716838352'>¶

class rbfopt_test_functions.hartman6_cat[source]¶

Bases: object

Hartman6 function of the Dixon-Szego test set, with categorical vars.

a = <Mock name='mock.array()' id='140550716838352'>¶

c = <Mock name='mock.array()' id='140550716838352'>¶

dimension = 7¶

classmethod evaluate(x)[source]¶

optimum_point = <Mock name='mock.array()' id='140550716838352'>¶

optimum_value = -3.96231691936822¶

p = <Mock name='mock.array()' id='140550716838352'>¶

var_lower = <Mock name='mock.array()' id='140550716838352'>¶

var_type = <Mock name='mock.array()' id='140550716838352'>¶

var_upper = <Mock name='mock.array()' id='140550716838352'>¶

class rbfopt_test_functions.least[source]¶

Bases: object

least function of the GlobalLib test set.

dimension = 3¶

classmethod evaluate(x)[source]¶

optimum_point = <Mock name='mock.array()' id='140550716838352'>¶

optimum_value = 14085.139848928¶

var_lower = <Mock name='mock.array()' id='140550716838352'>¶

var_type = <Mock name='mock.array()' id='140550716838352'>¶

var_upper = <Mock name='mock.array()' id='140550716838352'>¶

class rbfopt_test_functions.nvs02[source]¶

Bases: object

nvs02 function of the MINLPLib test set.

dimension = 5¶

classmethod evaluate(x)[source]¶

optimum_point = <Mock name='mock.array()' id='140550716838352'>¶

optimum_value = 5.92239325641¶

var_lower = <Mock name='mock.array()' id='140550716838352'>¶

var_type = <Mock name='mock.array()' id='140550716838352'>¶

var_upper = <Mock name='mock.array()' id='140550716838352'>¶

class rbfopt_test_functions.nvs03[source]¶

Bases: object

nvs03 function of the MINLPLib test set.

dimension = 2¶

classmethod evaluate(x)[source]¶

optimum_point = <Mock name='mock.array()' id='140550716838352'>¶

optimum_value = 16.0¶

var_lower = <Mock name='mock.array()' id='140550716838352'>¶

var_type = <Mock name='mock.array()' id='140550716838352'>¶

var_upper = <Mock name='mock.array()' id='140550716838352'>¶

class rbfopt_test_functions.nvs04[source]¶

Bases: object

nvs04 function of the MINLPLib test set.

dimension = 2¶

classmethod evaluate(x)[source]¶

optimum_point = <Mock name='mock.array()' id='140550716838352'>¶

optimum_value = 0.72¶

var_lower = <Mock name='mock.array()' id='140550716838352'>¶

var_type = <Mock name='mock.array()' id='140550716838352'>¶

var_upper = <Mock name='mock.array()' id='140550716838352'>¶

class rbfopt_test_functions.nvs06[source]¶

Bases: object

nvs06 function of the MINLPLib test set.

dimension = 2¶

classmethod evaluate(x)[source]¶

optimum_point = <Mock name='mock.array()' id='140550716838352'>¶

optimum_value = 1.7703125¶

var_lower = <Mock name='mock.array()' id='140550716838352'>¶

var_type = <Mock name='mock.array()' id='140550716838352'>¶

var_upper = <Mock name='mock.array()' id='140550716838352'>¶

class rbfopt_test_functions.nvs07[source]¶

Bases: object

nvs07 function of the MINLPLib test set.

dimension = 3¶

classmethod evaluate(x)[source]¶

optimum_point = <Mock name='mock.array()' id='140550716838352'>¶

optimum_value = 4.0¶

var_lower = <Mock name='mock.array()' id='140550716838352'>¶

var_type = <Mock name='mock.array()' id='140550716838352'>¶

var_upper = <Mock name='mock.array()' id='140550716838352'>¶

class rbfopt_test_functions.nvs07_cat[source]¶

Bases: object

nvs07 function of the MINLPLib test set, with categorical variables.

dimension = 4¶

classmethod evaluate(x)[source]¶

optimum_point = <Mock name='mock.array()' id='140550716838352'>¶

optimum_value = 2.0¶

var_lower = <Mock name='mock.array()' id='140550716838352'>¶

var_type = <Mock name='mock.array()' id='140550716838352'>¶

var_upper = <Mock name='mock.array()' id='140550716838352'>¶

class rbfopt_test_functions.nvs09[source]¶

Bases: object

nvs09 function of the MINLPLib test set.

dimension = 10¶

classmethod evaluate(x)[source]¶

optimum_point = <Mock name='mock.array()' id='140550716838352'>¶

optimum_value = -43.134336918035¶

var_lower = <Mock name='mock.array()' id='140550716838352'>¶

var_type = <Mock name='mock.array()' id='140550716838352'>¶

var_upper = <Mock name='mock.array()' id='140550716838352'>¶

class rbfopt_test_functions.nvs09_cat[source]¶

Bases: object

nvs09 function of the MINLPLib test set with categorical variables

dimension = 11¶

classmethod evaluate(x)[source]¶

optimum_point = <Mock name='mock.array()' id='140550716838352'>¶

optimum_value = -53.179649471788274¶

var_lower = <Mock name='mock.array()' id='140550716838352'>¶

var_type = <Mock name='mock.array()' id='140550716838352'>¶

var_upper = <Mock name='mock.array()' id='140550716838352'>¶

class rbfopt_test_functions.nvs14[source]¶

Bases: object

nvs14 function of the MINLPLib test set.

dimension = 5¶

classmethod evaluate(x)[source]¶

optimum_point = <Mock name='mock.array()' id='140550716838352'>¶

optimum_value = -40358.1547693¶

var_lower = <Mock name='mock.array()' id='140550716838352'>¶

var_type = <Mock name='mock.array()' id='140550716838352'>¶

var_upper = <Mock name='mock.array()' id='140550716838352'>¶

class rbfopt_test_functions.nvs15[source]¶

Bases: object

nvs15 function of the MINLPLib test set.

dimension = 3¶

classmethod evaluate(x)[source]¶

optimum_point = <Mock name='mock.array()' id='140550716838352'>¶

optimum_value = 1.0¶

var_lower = <Mock name='mock.array()' id='140550716838352'>¶

var_type = <Mock name='mock.array()' id='140550716838352'>¶

var_upper = <Mock name='mock.array()' id='140550716838352'>¶

class rbfopt_test_functions.nvs16[source]¶

Bases: object

nvs16 function of the MINLPLib test set.

dimension = 2¶

classmethod evaluate(x)[source]¶

optimum_point = <Mock name='mock.array()' id='140550716838352'>¶

optimum_value = 0.703125¶

var_lower = <Mock name='mock.array()' id='140550716838352'>¶

var_type = <Mock name='mock.array()' id='140550716838352'>¶

var_upper = <Mock name='mock.array()' id='140550716838352'>¶

class rbfopt_test_functions.perm0_8[source]¶

Bases: object

perm0 function of dimension 8 from Arnold Neumaier. http://www.mat.univie.ac.at/~neum/glopt/my_problems.html We use parameters (8, 100) here.

dimension = 8¶

classmethod evaluate(x)[source]¶

optimum_point = <Mock name='mock.array()' id='140550716838352'>¶

optimum_value = 1000.0¶

var_lower = <Mock name='mock.array()' id='140550716838352'>¶

var_type = <Mock name='mock.array()' id='140550716838352'>¶

var_upper = <Mock name='mock.array()' id='140550716838352'>¶

class rbfopt_test_functions.perm_6[source]¶

Bases: object

perm function of dimension 6 from Arnold Neumaier. http://www.mat.univie.ac.at/~neum/glopt/my_problems.html We use parameters (6, 60) here.

dimension = 6¶

classmethod evaluate(x)[source]¶

optimum_point = <Mock name='mock.array()' id='140550716838352'>¶

optimum_value = 1000.0¶

var_lower = <Mock name='mock.array()' id='140550716838352'>¶

var_type = <Mock name='mock.array()' id='140550716838352'>¶

var_upper = <Mock name='mock.array()' id='140550716838352'>¶

class rbfopt_test_functions.prob03[source]¶

Bases: object

prob03 function of the MINLPLib test set.

dimension = 2¶

classmethod evaluate(x)[source]¶

optimum_point = <Mock name='mock.array()' id='140550716838352'>¶

optimum_value = 10.0¶

var_lower = <Mock name='mock.array()' id='140550716838352'>¶

var_type = <Mock name='mock.array()' id='140550716838352'>¶

var_upper = <Mock name='mock.array()' id='140550716838352'>¶

class rbfopt_test_functions.rbrock[source]¶

Bases: object

rbrock function of the GlobalLib test set.

dimension = 2¶

classmethod evaluate(x)[source]¶

optimum_point = <Mock name='mock.array()' id='140550716838352'>¶

optimum_value = 0.0¶

var_lower = <Mock name='mock.array()' id='140550716838352'>¶

var_type = <Mock name='mock.array()' id='140550716838352'>¶

var_upper = <Mock name='mock.array()' id='140550716838352'>¶

class rbfopt_test_functions.schaeffer_f7_12_1[source]¶

Bases: object

Schaeffer F7 function.

dimension = 12¶

classmethod evaluate(x)[source]¶

optimum_point = <Mock name='mock.array()' id='140550716838352'>¶

optimum_value = -10¶

var_lower = <Mock name='mock.array()' id='140550716838352'>¶

var_type = <Mock name='mock.array()' id='140550716838352'>¶

var_upper = <Mock name='mock.array()' id='140550716838352'>¶

class rbfopt_test_functions.schaeffer_f7_12_1_int_cat[source]¶

Bases: object

Schaeffer F7 function with integer and categorical variables

dimension = 13¶

classmethod evaluate(x)[source]¶

optimum_point = <Mock name='mock.array()' id='140550716838352'>¶

optimum_value = -10¶

var_lower = <Mock name='mock.array()' id='140550716838352'>¶

var_type = <Mock name='mock.array()' id='140550716838352'>¶

var_upper = <Mock name='mock.array()' id='140550716838352'>¶

class rbfopt_test_functions.schaeffer_f7_12_2[source]¶

Bases: object

Schaeffer F7 function.

dimension = 12¶

classmethod evaluate(x)[source]¶

optimum_point = <Mock name='mock.array()' id='140550716838352'>¶

optimum_value = 10¶

var_lower = <Mock name='mock.array()' id='140550716838352'>¶

var_type = <Mock name='mock.array()' id='140550716838352'>¶

var_upper = <Mock name='mock.array()' id='140550716838352'>¶

class rbfopt_test_functions.schaeffer_f7_12_2_int_cat[source]¶

Bases: object

Schaeffer F7 function with integer and categorical variables.

dimension = 13¶

classmethod evaluate(x)[source]¶

optimum_point = <Mock name='mock.array()' id='140550716838352'>¶

optimum_value = -10¶

var_lower = <Mock name='mock.array()' id='140550716838352'>¶

var_type = <Mock name='mock.array()' id='140550716838352'>¶

var_upper = <Mock name='mock.array()' id='140550716838352'>¶

class rbfopt_test_functions.schoen_10_1[source]¶

Bases: object

schoen function of dimension 10 with 50 stationary points.

dimension = 10¶

classmethod evaluate(x)[source]¶

f = <Mock name='mock.array()' id='140550716838352'>¶

optimum_point = <Mock name='mock.array()' id='140550716838352'>¶

optimum_value = -1000¶

var_lower = <Mock name='mock.array()' id='140550716838352'>¶

var_type = <Mock name='mock.array()' id='140550716838352'>¶

var_upper = <Mock name='mock.array()' id='140550716838352'>¶

z = <Mock name='mock.array()' id='140550716838352'>¶

class rbfopt_test_functions.schoen_10_1_cat[source]¶

Bases: object

schoen function of dimension 10 with categorical variables.

dimension = 12¶

classmethod evaluate(x)[source]¶

f = <Mock name='mock.array()' id='140550716838352'>¶

optimum_point = <Mock name='mock.array()' id='140550716838352'>¶

optimum_value = -1000¶

var_lower = <Mock name='mock.array()' id='140550716838352'>¶

var_type = <Mock name='mock.array()' id='140550716838352'>¶

var_upper = <Mock name='mock.array()' id='140550716838352'>¶

z = <Mock name='mock.array()' id='140550716838352'>¶

class rbfopt_test_functions.schoen_10_1_int[source]¶

Bases: object

schoen function of dimension 10 with 50 stationary points.

Mixed integer version.

dimension = 10¶

classmethod evaluate(x)[source]¶

f = <Mock name='mock.array()' id='140550716838352'>¶

optimum_point = <Mock name='mock.array()' id='140550716838352'>¶

optimum_value = -1000¶

var_lower = <Mock name='mock.array()' id='140550716838352'>¶

var_type = <Mock name='mock.array()' id='140550716838352'>¶

var_upper = <Mock name='mock.array()' id='140550716838352'>¶

z = <Mock name='mock.array()' id='140550716838352'>¶

class rbfopt_test_functions.schoen_10_2[source]¶

Bases: object

schoen function of dimension 10 with 50 stationary points.

dimension = 10¶

classmethod evaluate(x)[source]¶

f = <Mock name='mock.array()' id='140550716838352'>¶

optimum_point = <Mock name='mock.array()' id='140550716838352'>¶

optimum_value = -1000¶

var_lower = <Mock name='mock.array()' id='140550716838352'>¶

var_type = <Mock name='mock.array()' id='140550716838352'>¶

var_upper = <Mock name='mock.array()' id='140550716838352'>¶

z = <Mock name='mock.array()' id='140550716838352'>¶

class rbfopt_test_functions.schoen_10_2_cat[source]¶

Bases: object

schoen function of dimension 10 with categorical variables.

dimension = 12¶

classmethod evaluate(x)[source]¶

f = <Mock name='mock.array()' id='140550716838352'>¶

optimum_point = <Mock name='mock.array()' id='140550716838352'>¶

optimum_value = -1000¶

var_lower = <Mock name='mock.array()' id='140550716838352'>¶

var_type = <Mock name='mock.array()' id='140550716838352'>¶

var_upper = <Mock name='mock.array()' id='140550716838352'>¶

z = <Mock name='mock.array()' id='140550716838352'>¶

class rbfopt_test_functions.schoen_10_2_int[source]¶

Bases: object

schoen function of dimension 10 with 50 stationary points.

Mixed integer version.

dimension = 10¶

classmethod evaluate(x)[source]¶

f = <Mock name='mock.array()' id='140550716838352'>¶

optimum_point = <Mock name='mock.array()' id='140550716838352'>¶

optimum_value = -1000¶

var_lower = <Mock name='mock.array()' id='140550716838352'>¶

var_type = <Mock name='mock.array()' id='140550716838352'>¶

var_upper = <Mock name='mock.array()' id='140550716838352'>¶

z = <Mock name='mock.array()' id='140550716838352'>¶

class rbfopt_test_functions.schoen_6_1[source]¶

Bases: object

schoen function of dimension 6 with 50 stationary points.

dimension = 6¶

classmethod evaluate(x)[source]¶

f = <Mock name='mock.array()' id='140550716838352'>¶

optimum_point = <Mock name='mock.array()' id='140550716838352'>¶

optimum_value = -1000¶

var_lower = <Mock name='mock.array()' id='140550716838352'>¶

var_type = <Mock name='mock.array()' id='140550716838352'>¶

var_upper = <Mock name='mock.array()' id='140550716838352'>¶

z = <Mock name='mock.array()' id='140550716838352'>¶

class rbfopt_test_functions.schoen_6_1_int[source]¶

Bases: object

schoen function of dimension 6 with 50 stationary points.

Mixed integer version.

dimension = 6¶

classmethod evaluate(x)[source]¶

f = <Mock name='mock.array()' id='140550716838352'>¶

optimum_point = <Mock name='mock.array()' id='140550716838352'>¶

optimum_value = -1000¶

var_lower = <Mock name='mock.array()' id='140550716838352'>¶

var_type = <Mock name='mock.array()' id='140550716838352'>¶

var_upper = <Mock name='mock.array()' id='140550716838352'>¶

z = <Mock name='mock.array()' id='140550716838352'>¶

class rbfopt_test_functions.schoen_6_2[source]¶

Bases: object

schoen function of dimension 6 with 50 stationary points.

dimension = 6¶

classmethod evaluate(x)[source]¶

f = <Mock name='mock.array()' id='140550716838352'>¶

optimum_point = <Mock name='mock.array()' id='140550716838352'>¶

optimum_value = -1000¶

var_lower = <Mock name='mock.array()' id='140550716838352'>¶

var_type = <Mock name='mock.array()' id='140550716838352'>¶

var_upper = <Mock name='mock.array()' id='140550716838352'>¶

z = <Mock name='mock.array()' id='140550716838352'>¶

class rbfopt_test_functions.schoen_6_2_int[source]¶

Bases: object

schoen function of dimension 6 with 50 stationary points.

Mixed integer version.

dimension = 6¶

classmethod evaluate(x)[source]¶

f = <Mock name='mock.array()' id='140550716838352'>¶

optimum_point = <Mock name='mock.array()' id='140550716838352'>¶

optimum_value = -1000¶

var_lower = <Mock name='mock.array()' id='140550716838352'>¶

var_type = <Mock name='mock.array()' id='140550716838352'>¶

var_upper = <Mock name='mock.array()' id='140550716838352'>¶

z = <Mock name='mock.array()' id='140550716838352'>¶

class rbfopt_test_functions.shekel10[source]¶

Bases: object

Shekel10 function of the Dixon-Szego test set.

a = [[4.0, 1.0, 8.0, 6.0, 3.0, 2.0, 5.0, 8.0, 6.0, 7.0], [4.0, 1.0, 8.0, 6.0, 7.0, 9.0, 5.0, 1.0, 2.0, 3.6], [4.0, 1.0, 8.0, 6.0, 3.0, 2.0, 3.0, 8.0, 6.0, 7.0], [4.0, 1.0, 8.0, 6.0, 7.0, 9.0, 3.0, 1.0, 2.0, 3.6]]¶

c = [0.1, 0.2, 0.2, 0.4, 0.4, 0.6, 0.3, 0.7, 0.5, 0.5]¶

dimension = 4¶

classmethod evaluate(x)[source]¶

optimum_point = <Mock name='mock.array()' id='140550716838352'>¶

optimum_value = -10.5362837262196¶

var_lower = <Mock name='mock.array()' id='140550716838352'>¶

var_type = <Mock name='mock.array()' id='140550716838352'>¶

var_upper = <Mock name='mock.array()' id='140550716838352'>¶

class rbfopt_test_functions.shekel5[source]¶

Bases: object

Shekel5 function of the Dixon-Szego test set.

a = [[4.0, 1.0, 8.0, 6.0, 3.0], [4.0, 1.0, 8.0, 6.0, 7.0], [4.0, 1.0, 8.0, 6.0, 3.0], [4.0, 1.0, 8.0, 6.0, 7.0]]¶

c = [0.1, 0.2, 0.2, 0.4, 0.4]¶

dimension = 4¶

classmethod evaluate(x)[source]¶

optimum_point = <Mock name='mock.array()' id='140550716838352'>¶

optimum_value = -10.153195850979¶

var_lower = <Mock name='mock.array()' id='140550716838352'>¶

var_type = <Mock name='mock.array()' id='140550716838352'>¶

var_upper = <Mock name='mock.array()' id='140550716838352'>¶

class rbfopt_test_functions.shekel7[source]¶

Bases: object

Shekel7 function of the Dixon-Szego test set.

a = [[4.0, 1.0, 8.0, 6.0, 3.0, 2.0, 5.0], [4.0, 1.0, 8.0, 6.0, 7.0, 9.0, 5.0], [4.0, 1.0, 8.0, 6.0, 3.0, 2.0, 3.0], [4.0, 1.0, 8.0, 6.0, 7.0, 9.0, 3.0]]¶

c = [0.1, 0.2, 0.2, 0.4, 0.4, 0.6, 0.3]¶

dimension = 4¶

classmethod evaluate(x)[source]¶

optimum_point = <Mock name='mock.array()' id='140550716838352'>¶

optimum_value = -10.4028188369303¶

var_lower = <Mock name='mock.array()' id='140550716838352'>¶

var_type = <Mock name='mock.array()' id='140550716838352'>¶

var_upper = <Mock name='mock.array()' id='140550716838352'>¶

class rbfopt_test_functions.sporttournament06[source]¶

Bases: object

sporttournament06 function of the MINLPLib test set.

dimension = 15¶

classmethod evaluate(x)[source]¶

optimum_point = <Mock name='mock.array()' id='140550716838352'>¶

optimum_value = -12.0¶

var_lower = <Mock name='mock.array()' id='140550716838352'>¶

var_type = <Mock name='mock.array()' id='140550716838352'>¶

var_upper = <Mock name='mock.array()' id='140550716838352'>¶

class rbfopt_test_functions.st_miqp1[source]¶

Bases: object

st_miqp1 function of the MINLPLib test set.

dimension = 5¶

classmethod evaluate(x)[source]¶

optimum_point = <Mock name='mock.array()' id='140550716838352'>¶

optimum_value = 281.0¶

var_lower = <Mock name='mock.array()' id='140550716838352'>¶

var_type = <Mock name='mock.array()' id='140550716838352'>¶

var_upper = <Mock name='mock.array()' id='140550716838352'>¶

class rbfopt_test_functions.st_miqp1_cat[source]¶

Bases: object

st_miqp1 function of the MINLPLib test set, with categorical variables.

c = <Mock name='mock.array()' id='140550716838352'>¶

dimension = 6¶

classmethod evaluate(x)[source]¶

optimum_point = <Mock name='mock.array()' id='140550716838352'>¶

optimum_value = 186.0¶

var_lower = <Mock name='mock.array()' id='140550716838352'>¶

var_type = <Mock name='mock.array()' id='140550716838352'>¶

var_upper = <Mock name='mock.array()' id='140550716838352'>¶

class rbfopt_test_functions.st_miqp3[source]¶

Bases: object

st_miqp3 function of the MINLPLib test set.

dimension = 2¶

classmethod evaluate(x)[source]¶

optimum_point = <Mock name='mock.array()' id='140550716838352'>¶

optimum_value = -6.0¶

var_lower = <Mock name='mock.array()' id='140550716838352'>¶

var_type = <Mock name='mock.array()' id='140550716838352'>¶

var_upper = <Mock name='mock.array()' id='140550716838352'>¶

class rbfopt_test_functions.st_test1[source]¶

Bases: object

st_test1 function of the MINLPLib test set.

dimension = 5¶

classmethod evaluate(x)[source]¶

optimum_point = <Mock name='mock.array()' id='140550716838352'>¶

optimum_value = 0.0¶

var_lower = <Mock name='mock.array()' id='140550716838352'>¶

var_type = <Mock name='mock.array()' id='140550716838352'>¶

var_upper = <Mock name='mock.array()' id='140550716838352'>¶