Description Usage Arguments Value Note Examples

View source: R/performGradientStep.R

Computes the bi-objective gradient for the two objectives `fn1`

and `fn2`

in position `ind`

. The bi-objective gradient is the combined vector of the
two single gradients. Note that the step size depends on the length of the combined
gradient vector and thus automatically decreases when approaching an efficient set.

1 2 3 | ```
performGradientStep(ind, fn1, fn2, gradient.list = list(g1 = NULL, g2 =
NULL), scale.step = 0.5, prec.grad = 1e-06, prec.norm = 1e-06,
prec.angle = 1e-04, lower, upper, check.data = TRUE)
``` |

`ind` |
[ |

`fn1` |
[ |

`fn2` |
[ |

`gradient.list` |
[ |

`scale.step` |
[ |

`prec.grad` |
[ |

`prec.norm` |
[ |

`prec.angle` |
[ |

`lower` |
[ |

`upper` |
[ |

`check.data` |
[ |

[`numeric`

(d) | `NULL`

]

Returns `NULL`

if `ind`

is a local efficient point. Otherwise a numeric
vector of length `d`

will be returned, which shows the result of a downhill
step in the direction of the multi-objective gradient.

ATTENTION: Only turn off the sanity checks (`check.data = FALSE`

),
if you can ensure that all input parameters are provided in the correct format.

1 2 3 4 5 6 7 8 9 | ```
# Define two single-objective test problems:
fn1 = function(x) sum((x - c(0.2, 1))^2)
fn2 = function(x) sum(x)
# Perform a gradient step:
performGradientStep(c(0.3, 0.5), fn1, fn2)
# Here, we have found the optimum of fn1:
performGradientStep(c(0.2, 1), fn1, fn2)
``` |

kerschke/mogsa documentation built on Oct. 27, 2018, 12:13 a.m.

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