desdeo.utils

DESDEO Utilities

desdeo.utils.as_minimized(values, maximized)[source]

Return vector values as minimized

Return type

List[float]

desdeo.utils.isin(value, values)[source]

Check that value is in values

desdeo.utils.new_points(factory, solution, weights=None)[source]

Generate approximate set of points

Generate set of Pareto optimal solutions projecting from the Pareto optimal solution using weights to determine the direction.

Parameters

  • factory (IterationPointFactory) – IterationPointFactory with suitable optimization problem

  • solution – Current solution from which new solutions are projected

  • weights (Optional[List[List[float]]]) – Direction of the projection, if not given generate with :func:random_weights

Return type

List[Tuple[ndarray, List[float]]]

desdeo.utils.random_weights(nobj, nweight)[source]

Generatate nw random weight vectors for nof objectives as per Tchebycheff method [SteCho83]

SteCho83

Steuer, R. E. & Choo, E.-U. An interactive weighted Tchebycheff procedure for multiple objective programming, Mathematical programming, Springer, 1983, 26, 326-344

Parameters

  • nobj (int) – Number of objective functions

  • nweight (int) – Number of weights vectors to be generated

Returns

nobj x nweight matrix of weight vectors

Return type

List[List[float]

desdeo.utils.reachable_points(points, lower, upper)[source]