SCGoptim: Optimise the given function using (scaled) conjugate...

In ahonkela/tigre: Transcription factor Inference through Gaussian process Reconstruction of Expression

Description

Optimise the given function using (scaled) conjugate gradients.

Usage

  optimiDefaultOptions()
  SCGoptim(x, fn, grad, options, ...)
  CGoptim(x, fn, grad, options, ...)
  modelOptimise(model, options, ...)

Arguments

model	the model to be optimised.
x	initial parameter values.
fn	objective function to minimise
grad	gradient function of the objective
options	options structure like one returned by optimiDefaultOptions. The fields are interpreted as\ option[1] : number of iterations\ option[2] : interval for the line search\ option[3] : tolerence for x to terminate the loop\ option[4] : tolerence for fn to terminate the loop\ option$display : option of showing the details of optimisaton
...	extra arguments to pass to fn and grad

Value

options	an options structure
newParams	optimised parameter values
model	the optimised model.

See Also

modelObjective, modelGradient

Examples

## Not run to speed up package checks
# model <- GPLearn(..., dontOptimise=TRUE)
# options <- optimiDefaultOptions()
# model <- modelOptimise(model, options)

ahonkela/tigre documentation built on Aug. 6, 2021, 12:08 p.m.