snewton: Safeguarded Newton methods for function minimization using R...

In optimx: Expanded Replacement and Extension of the 'optim' Function

Safeguarded Newton methods for function minimization using R functions.

Description

These versions of the safeguarded Newton solve the Newton equations with the R function solve(). In snewton a backtracking line search is used, while in snewtonm we rely on a Marquardt stabilization.

Usage

   snewton(par, fn, gr, hess, control = list(trace=0, maxit=500), ...)

   snewtm(par, fn, gr, hess, bds, control = list(trace=0, maxit=500))

Arguments

par	A numeric vector of starting estimates.
fn	A function that returns the value of the objective at the supplied set of parameters par using auxiliary data in .... The first argument of fn must be par.
gr	A function that returns the gradient of the objective at the supplied set of parameters par using auxiliary data in .... The first argument of fn must be par. This function returns the gradient as a numeric vector.
hess	A function to compute the Hessian matrix. This should be provided as a square, symmetric matrix.
bds	Result of bmchk() for the current problem. Contains lower and upper etc.
control	An optional list of control settings.
...	Further arguments to be passed to fn.

Details

snewtm is intended to be called from optimr().

Functions fn must return a numeric value. gr must return a vector. hess must return a matrix. The control argument is a list. See the code for snewton.R for completeness. Some of the values that may be important for users are:

trace

Set 0 (default) for no output, > 0 for diagnostic output (larger values imply more output).

watch

Set TRUE if the routine is to stop for user input (e.g., Enter) after each iteration. Default is FALSE.

maxit

A limit on the number of iterations (default 500 + 2*n where n is the number of parameters). This is the maximum number of gradient evaluations allowed.

maxfeval

A limit on the number of function evaluations allowed (default 3000 + 10*n).

eps

a tolerance used for judging small gradient norm (default = 1e-07). a gradient norm smaller than (1 + abs(fmin))*eps*eps is considered small enough that a local optimum has been found, where fmin is the current estimate of the minimal function value.

acctol

To adjust the acceptable point tolerance (default 0.0001) in the test ( f <= fmin + gradproj * steplength * acctol ). This test is used to ensure progress is made at each iteration.

stepdec

Step reduction factor for backtrack line search (default 0.2)

defstep

Initial stepsize default (default 1)

reltest

Additive shift for equality test (default 100.0)

The (unconstrained) solver snewtonmu proved to be slower than the bounded solver called without bounds, so has been withdrawn.

The snewton safeguarded Newton uses a simple line search but no linear solution stabilization and has demonstrated POOR performance and reliability. NOT recommended.

Value

A list with components:

par

The best set of parameters found.

value

The value of the objective at the best set of parameters found.

grad

The value of the gradient at the best set of parameters found. A vector.

hessian

The value of the Hessian at the best set of parameters found. A matrix.

counts

A vector of 4 integers giving number of Newton equation solutions, the number of function evaluations, the number of gradient evaluations and the number of hessian evaluations.

message

A message giving some information on the status of the solution.

References

Nash, J C (1979, 1990) Compact Numerical Methods for Computers: Linear Algebra and Function Minimisation, Bristol: Adam Hilger. Second Edition, Bristol: Institute of Physics Publications.

See Also

optim

Examples

#Rosenbrock banana valley function
f <- function(x){
return(100*(x[2] - x[1]*x[1])^2 + (1-x[1])^2)
}
#gradient
gr <- function(x){
return(c(-400*x[1]*(x[2] - x[1]*x[1]) - 2*(1-x[1]), 200*(x[2] - x[1]*x[1])))
}
#Hessian
h <- function(x) {
a11 <- 2 - 400*x[2] + 1200*x[1]*x[1]; a21 <- -400*x[1]
return(matrix(c(a11, a21, a21, 200), 2, 2))
}

fg <- function(x){ #function and gradient
  val <- f(x)
  attr(val,"gradient") <- gr(x)
  val
}
fgh <- function(x){ #function and gradient
  val <- f(x)
  attr(val,"gradient") <- gr(x)
  attr(val,"hessian") <- h(x)
  val
}

x0 <- c(-1.2, 1)

sr <- snewton(x0, fn=f, gr=gr, hess=h, control=list(trace=1))
print(sr)
# Call through optimr to get correct calling sequence, esp. with bounds
srm <- optimr(x0, fn=f, gr=gr, hess=h, control=list(trace=1))
print(srm)

# bounds constrained example

lo <- rep((min(x0)-0.1), 2)
up <- rep((max(x0)+0.1), 2)
# Call through optimr to get correct calling sequence, esp. with bounds
srmb <- optimr(x0, fn=f, gr=gr, hess=h, lower=lo, upper=up, control=list(trace=1))
proptimr(srmb)


#Example 2: Wood function
#
wood.f <- function(x){
  res <- 100*(x[1]^2-x[2])^2+(1-x[1])^2+90*(x[3]^2-x[4])^2+(1-x[3])^2+
    10.1*((1-x[2])^2+(1-x[4])^2)+19.8*(1-x[2])*(1-x[4])
  return(res)
}
#gradient:
wood.g <- function(x){
  g1 <- 400*x[1]^3-400*x[1]*x[2]+2*x[1]-2
  g2 <- -200*x[1]^2+220.2*x[2]+19.8*x[4]-40
  g3 <- 360*x[3]^3-360*x[3]*x[4]+2*x[3]-2
  g4 <- -180*x[3]^2+200.2*x[4]+19.8*x[2]-40
  return(c(g1,g2,g3,g4))
}
#hessian:
wood.h <- function(x){
  h11 <- 1200*x[1]^2-400*x[2]+2;    h12 <- -400*x[1]; h13 <- h14 <- 0
  h22 <- 220.2; h23 <- 0;    h24 <- 19.8
  h33 <- 1080*x[3]^2-360*x[4]+2;    h34 <- -360*x[3]
  h44 <- 200.2
  H <- matrix(c(h11,h12,h13,h14,h12,h22,h23,h24,
                h13,h23,h33,h34,h14,h24,h34,h44),ncol=4)
  return(H)
}
#################################################
w0 <- c(-3, -1, -3, -1)

wd <- snewton(w0, fn=wood.f, gr=wood.g, hess=wood.h, control=list(trace=1))
print(wd)

# Call through optimr to get correct calling sequence, esp. with bounds
wdm <- optimr(w0, fn=wood.f, gr=wood.g, hess=wood.h, control=list(trace=1))
print(wdm)

optimx documentation built on Oct. 24, 2023, 5:06 p.m.