SCGoptim {tigre} | R Documentation

## Optimise the given function using (scaled) conjugate gradients.

### 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)

[Package tigre version 1.36.0 Index]