computeGradientField: Compute the multi-objective gradient vector for a set of...

In kerschke/moPLOT: Visualizing Multi-Objective Optimization Problems

Compute the multi-objective gradient vector for a set of points.

Description

Computes the multi-objective gradients for a matrix of points.

Usage

computeGradientField(
  points,
  fn,
  prec.grad = 1e-06,
  prec.norm = 1e-06,
  prec.angle = 1e-04,
  parallelize = FALSE,
  impute.boundary = TRUE,
  lower = NULL,
  upper = NULL,
  verbose = TRUE
)

Arguments

points	[matrix] Matrix of points, for which the multi-objective gradient should be computed. Each row of the matrix will be considered as a separate point, thus the number of rows corresponds to the number of observations and the number of columns to the dimensionality of the search space.
fn	[function] Multi-objective function with up to three objectives.
prec.grad	[numeric] Precision value (= step size) used for approximating the gradient. The default is 1e-6.
prec.norm	[numeric] Precision threshold when normalizing a vector. That is, every element of the vector, whose absolute value is below this threshold, will be replaced by 0. The default is 1e-6.
prec.angle	[numeric] Precision threshold used for comparing whether the angle (in degree) between two vectors is zero. The default is 1e-4.
parallelize	[logical] Should the computation of the gradient vectors be parallelized (with parallel::mclapply)? The default is FALSE.
impute.boundary	[logical] Should values at the boundary be imputed? This is relevant for problems that have locally efficient points on the boundary box.
lower	[numeric] Vector of lower bounds.
upper	[numeric] Vector of upper bounds.

Value

[matrix]
Returns matrix of multi-objective gradients. The i-th row of the matrix contains the multi-objective gradient vector of the i-th observation (= row) of the input matrix points.

kerschke/moPLOT documentation built on Aug. 23, 2023, 7:37 p.m.