pymoto.SLP

class pymoto.SLP(variables: Signal | Iterable[Signal], responses: Signal | Iterable[Signal], function: Network, slice_network: bool = False, move=0.1, xmin=0.0, xmax=1.0, verbosity: int = 2, adaptive_movelimit: bool = True, **kwargs)
__init__(variables: Signal | Iterable[Signal], responses: Signal | Iterable[Signal], function: Network, slice_network: bool = False, move=0.1, xmin=0.0, xmax=1.0, verbosity: int = 2, adaptive_movelimit: bool = True, **kwargs)

SLP optimization algorithm Warning: This optimizer is experimental and is not very robust (yet)

Parameters:

  • variables – One or more variable Signals defining the design variables

  • responses – One or more response Signals, where the first is to be minimized and the others are constraints in negative null form.

  • function – The Network defining the optimization problem

Keyword Arguments:

  • slice_network – If True, only the modules connecting variable and response signals are evaluated

  • move – Move limit on relative variable change per iteration (can be passed as scalar: same value for all variables, vector: each variable has a unique value, list of scalars/vectors: for each variable signal)

  • xmin – Minimum design variable (can be passed as scalar: same value for all variables, vector: each variable has a unique value, list of scalars/vectors: for each variable signal)

  • xmax – Maximum design variable (can be passed as scalar: same value for all variables, vector: each variable has a unique value, list of scalars/vectors: for each variable signal)

  • adaptive_movelimit (bool) – Move limit is adapted based on variable oscillation behavior. Defaults to True.

  • asyincr (float) – Increase of adaptive movelimit when no oscillation is present. Default is 1.2

  • asydecr (float) – Decrease in adaptive movelimit when variable is oscillating. Default is 0.7

  • asyinit (float) – Initial adaptive movelimit value. Default is 1.0

  • asybound (float) – Lower bound on adaptive movelimit. Default is 1e-2

Methods

__init__(variables, responses, function[, ...])

SLP optimization algorithm Warning: This optimizer is experimental and is not very robust (yet)

calculate_dg()

Calculate Jacobian dg/dx

calculate_g()

Calculate function response g(x)

optimize([maxiter, tolx, tolf, evaluate_last])

Perform a gradient-based optimization

print_iteration_info(g[, ...])

Print iteration information

print_variable_info([dg])

Print information on variables (and sensitivities)

step(x, g, dg)

Performs a single optimization step

Attributes

x

Set current design variable vector
calculate_dg()

Calculate Jacobian dg/dx

calculate_g()

Calculate function response g(x)

optimize(maxiter: int = 100, tolx: float = 0.0001, tolf: float = 0.0001, evaluate_last: bool = False)

Perform a gradient-based optimization

Parameters:

  • maxiter (int, optional) – Maximum number of iteration. Defaults to 100.

  • tolx (float, optional) – Stopping criterium for relative design change. Defaults to 1e-4.

  • tolf (float, optional) – Stopping criterium for relative objective change. Defaults to 1e-4.

print_iteration_info(g: ndarray, report_feasibility: bool = False, xold: ndarray = None, xnew: ndarray = None)

Print iteration information

Parameters:

  • g (np.ndarray) – Response values

  • report_feasibility (bool, optional) – Print # violated constraints and worst value. Defaults to False.

  • xold (np.ndarray, optional) – If provided, shows information on design change. Defaults to None.

  • xnew (np.ndarray, optional) – If provided, shows information on design change. Defaults to None.

print_variable_info(dg: ndarray = None)

Print information on variables (and sensitivities)

Parameters:

dg (np.ndarray, optional) – If provided, information on sensitivities will also be printed. Defaults to None.

property x

Set current design variable vector

step(x: ndarray = None, g: ndarray = None, dg: ndarray = None) -> (<class 'numpy.ndarray'>, <class 'numpy.ndarray'>, <class 'numpy.ndarray'>)

Performs a single optimization step

Parameters:

  • x (np.ndarray, optional) – The design vector to evaluate. When not provided, use information set in state

  • g (np.ndarray, optional) – The function values to use. When not provided, will be calculated on demand

  • dg (np.ndarray, optional) – The sensitivity values to use. When not provided, will be calculated on demand

Returns:

New design vector g: (Evaluated) response values dg: (Evaluated) design sensitivities

Return type:

xnew