Title: Port of the 'Scilab' 'n1qn1' Module for Unconstrained BFGS Optimization
Version: 6.0.1-12
Maintainer: Matthew Fidler <matthew.fidler@gmail.com>
Description: Provides 'Scilab' 'n1qn1'. This takes more memory than traditional L-BFGS. The n1qn1 routine is useful since it allows prespecification of a Hessian. If the Hessian is near enough the truth in optimization it can speed up the optimization problem. The algorithm is described in the 'Scilab' optimization documentation located at https://www.scilab.org/sites/default/files/optimization_in_scilab.pdf. This version uses manually modified code from 'f2c' to make this a C only binary.
URL: https://github.com/nlmixr2/n1qn1c
BugReports: https://github.com/nlmixr2/n1qn1c/issues
Depends: R (≥ 3.2)
Imports: Rcpp (≥ 0.12.3)
Suggests: testthat, covr
License: CeCILL-2
Biarch: true
NeedsCompilation: yes
LinkingTo: RcppArmadillo (≥ 0.5.600.2.0), Rcpp (≥ 0.12.3)
Encoding: UTF-8
RoxygenNote: 7.3.2
Packaged: 2024-09-17 21:50:00 UTC; matt
Author: Matthew Fidler [aut, cre], Wenping Wang [aut], Claude Lemarechal [aut, ctb], Joseph Bonnans [ctb], Jean-Charles Gilbert [ctb], Claudia Sagastizabal [ctb], Stephen L. Campbell, [ctb], Jean-Philippe Chancelier [ctb], Ramine Nikoukhah [ctb], Dirk Eddelbuettel [ctb], Bruno Jofret [ctb], INRIA [cph]
Repository: CRAN
Date/Publication: 2024-09-17 22:20:02 UTC

This gives the function pointers in the n1qn1 library

Description

Using this will allow C-level linking by function pointers instead of abi.

Usage

.n1qn1ptr()

Value

list of pointers to the n1qn1 functions

Author(s)

Matthew L. Fidler

Examples


.n1qn1ptr()

n1qn1 optimization

Description

This is an R port of the n1qn1 optimization procedure in scilab.

Usage

n1qn1(
  call_eval,
  call_grad,
  vars,
  environment = parent.frame(1),
  ...,
  epsilon = .Machine$double.eps,
  max_iterations = 100,
  nsim = 100,
  imp = 0,
  invisible = NULL,
  zm = NULL,
  restart = FALSE,
  assign = FALSE,
  print.functions = FALSE
)

Arguments

call_eval

Objective function

call_grad

Gradient Function

vars

Initial starting point for line search

environment

Environment where call_eval/call_grad are evaluated.

...

Ignored additional parameters.

epsilon

Precision of estimate

max_iterations

Number of iterations

nsim

Number of function evaluations

imp

Verbosity of messages.

invisible

boolean to control if the output of the minimizer is suppressed.

zm

Prior Hessian (in compressed format; This format is output in c.hess).

restart

Is this an estimation restart?

assign

Assign hessian to c.hess in environment environment? (Default FALSE)

print.functions

Boolean to control if the function value and parameter estimates are echoed every time a function is called.

Value

The return value is a list with the following elements:

Author(s)

C. Lemarechal, Stephen L. Campbell, Jean-Philippe Chancelier, Ramine Nikoukhah, Wenping Wang & Matthew L. Fidler

Examples


## Rosenbrock's banana function
n=3; p=100

fr = function(x)
{
    f=1.0
    for(i in 2:n) {
        f=f+p*(x[i]-x[i-1]**2)**2+(1.0-x[i])**2
    }
    f
}

grr = function(x)
{
    g = double(n)
    g[1]=-4.0*p*(x[2]-x[1]**2)*x[1]
    if(n>2) {
        for(i in 2:(n-1)) {
            g[i]=2.0*p*(x[i]-x[i-1]**2)-4.0*p*(x[i+1]-x[i]**2)*x[i]-2.0*(1.0-x[i])
        }
    }
    g[n]=2.0*p*(x[n]-x[n-1]**2)-2.0*(1.0-x[n])
    g
}

x = c(1.02,1.02,1.02)
eps=1e-3
n=length(x); niter=100L; nsim=100L; imp=3L;
nzm=as.integer(n*(n+13L)/2L)
zm=double(nzm)

(op1 <- n1qn1(fr, grr, x, imp=3))

## Note there are 40 function calls and 40 gradient calls in the above optimization

## Now assume we know something about the Hessian:
c.hess <- c(797.861115,
            -393.801473,
            -2.795134,
            991.271179,
            -395.382900,
            200.024349)
c.hess <- c(c.hess, rep(0, 24 - length(c.hess)))

(op2 <- n1qn1(fr, grr, x,imp=3, zm=c.hess))

## Note with this knowledge, there were only 29 function/gradient calls

(op3 <- n1qn1(fr, grr, x, imp=3, zm=op1$c.hess))

## The number of function evaluations is still reduced because the Hessian
## is closer to what it should be than the initial guess.

## With certain optimization procedures this can be helpful in reducing the
## Optimization time.