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

Description Usage Arguments Value See Also Examples

Description

Optimise the given function using (scaled) conjugate gradients.

Usage

1
2
3
4
  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

1
2
3
4
## 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.