ugHgenb: Generate gradient and Hessian for a function at given...

In optfntools: Tools for assisting the preparation and testing of objective functions for optimization.

Description

ugHgenb is used to generate the gradient and Hessian of an objective function used for optimization. If a user-provided gradient function gr is available it is used to compute the gradient via the wrapper ugr, otherwise package numDeriv is used. If a user-provided Hessian function hess is available, it is used to compute a Hessian via the wrapper uhess. However, we do not allow the user Hessian function to be specified if the user gradient function is NULL. If the user gr is available, we use the function jacobian() from package numDeriv to compute the Hessian. In both these cases we check for symmetry of the Hessian. Computational Hessians are commonly NOT symmetric. If only the objective function fn is provided, then the Hessian is approximated with the function hessian from package numDeriv which guarantees a symmetric matrix.

Usage

  ugHgenb(par, fnuser=NULL, bdmsk=NULL, lower=NULL, upper=NULL,numgrad=FALSE,
      control=list()) 

Arguments

par	Set of parameters, assumed to be at a minimum of the function fn.
fnuser	Name of the list that has fn=user_objective_function, gr=user_gradient and hess=user_hessian. Note that gr or (gr and hess) may be NULL. While the default for this parameter is NULL, it MUST be provided if ugHgenb is to return a useful answer. The NULL is provided to ensure we do not inadvertently use an existing object in the scope of the function.
bdmsk	An integer vector of the same length as par. When an element of this vector is 0, the corresponding parameter value is fixed (masked) during an optimization. Non-zero values indicate a parameter is free (1), at a lower bound (-3) or at an upper bound (-1), but this routine only uses 0 values. ?? Do we want to use the mskidx used in some other routines?? which??
lower	Lower bounds for parameters in par.
upper	Upper bounds for parameters in par.
control	A list of controls to the function. Currently asymptol (default of 1.0e-7 which tests for asymmetry of Hessian approximation (see code for details of the test); ktrace, an integer, 0 gives no output, higher values give more information to monitor progress, and stoponerror, defaulting to FALSE to NOT stop when there is an error or asymmetry of Hessian. Set TRUE to stop.
numgrad	TRUE if we are using numerical gradient approximations.

Details

None

Value

ansout a list of four items,

• gn The approximation to the gradient vector.

• Hn The approximation to the Hessian matrix.

• gradOK TRUE if the gradient has been computed acceptably. FALSE otherwise.

• hessOK TRUE if the gradient has been computed acceptably and passes the symmetry test. FALSE otherwise.

• nbm The number of active bounds and masks.

Examples

cat("tugHgenb 120517\n")
# require(optfntools)

# source("/home/work/R-optimtest/xdevel/optfntools/R/ugHgenb.R")

cat("Rosenbrock, unscaled optimx default\n")

fr <- function(x) {   ## Rosenbrock Banana function
    x1 <- x[1]
    x2 <- x[2]
    100 * (x2 - x1 * x1)^2 + (1 - x1)^2
}
grr <- function(x) { ## Gradient of 'fr'
    x1 <- x[1]
    x2 <- x[2]
    c(-400 * x1 * (x2 - x1 * x1) - 2 * (1 - x1),
       200 *      (x2 - x1 * x1))
}
trad<-c(-1.2,1)
print(trad)
rf<-fr(trad)
rg<-grr(trad)
print(rf)
print(rg)
npar<-2
opxfn<-list2env(list(fn=fr, gr=grr, hess=NULL, MAXIMIZE=FALSE, PARSCALE=rep(1,npar), FNSCALE=1,
       KFN=0, KGR=0, KHESS=0))

# for gs=1 equivalence 20120410
fr1<-function(x){ x1<-x[1]; x2<-x[2]; (x2-x1*x1)^2+(1-x1)^2}

cat("Now the ugHgenb values\n")
ans1<-ugHgenb(trad, fnuser=opxfn, control=list(ktrace=2))
print(ans1)
cat("Comparisons\n")
cat("Gradient max abs difference: ", max(abs(rg-ans1$gn)),"\n")
rh<-jacobian(grr, trad)
cat("Hessiant max abs difference: ", max(abs(rh-ans1$Hn)),"\n")
cat("\n\n")
rm(opxfn)
tmp<-readline("now try genrose")

# genrosa function code -- attempts to match the rosenbrock at gs=100 and x=c(-1.2,1)
genrosa.f<- function(x, gs=NULL){ # objective function
## One generalization of the Rosenbrock banana valley function (n parameters)
    n <- length(x)
        if(is.null(gs)) { gs=100.0 }
        # Note do not at 1.0 so min at 0
    fval<-sum (gs*(x[1:(n-1)]^2 - x[2:n])^2 + (x[1:(n-1)] - 1)^2)
}

genrosa.g <- function(x, gs=NULL){
# vectorized gradient for genrose.f
# Ravi Varadhan 2009-04-03
    n <- length(x)
        if(is.null(gs)) { gs=100.0 }
    gg <- as.vector(rep(0, n))
    tn <- 2:n
    tn1 <- tn - 1
    z1 <- x[tn] - x[tn1]^2
    z2 <- 1 - x[tn1]
        # f = gs*z1*z1 + z2*z2
    gg[tn] <- 2 * (gs * z1)
    gg[tn1] <- gg[tn1] - 4 * gs * x[tn1] * z1 - 2 *z2 
    return(gg)
}

genrosa.h <- function(x, gs=NULL) { ## compute Hessian
   if(is.null(gs)) { gs=100.0 }
    n <- length(x)
    hh<-matrix(rep(0, n*n),n,n)
    for (i in 2:n) {
        z1<-x[i]-x[i-1]*x[i-1]
#        z2<-1.0 - x[i-1]
                hh[i,i]<-hh[i,i]+2.0*(gs+1.0)
                hh[i-1,i-1]<-hh[i-1,i-1]-4.0*gs*z1-4.0*gs*x[i-1]*(-2.0*x[i-1])
                hh[i,i-1]<-hh[i,i-1]-4.0*gs*x[i-1]
                hh[i-1,i]<-hh[i-1,i]-4.0*gs*x[i-1]
    }
        return(hh)
}

# genrose function code
genrose.f<- function(x, gs=NULL){ # objective function
## One generalization of the Rosenbrock banana valley function (n parameters)
    n <- length(x)
        if(is.null(gs)) { gs=100.0 }
    fval<-1.0 + sum (gs*(x[1:(n-1)]^2 - x[2:n])^2 + (x[2:n] - 1)^2)
        return(fval)
}

genrose.g <- function(x, gs=NULL){
# vectorized gradient for genrose.f
# Ravi Varadhan 2009-04-03
    n <- length(x)
        if(is.null(gs)) { gs=100.0 }
    gg <- as.vector(rep(0, n))
    tn <- 2:n
    tn1 <- tn - 1
    z1 <- x[tn] - x[tn1]^2
    z2 <- 1 - x[tn]
    gg[tn] <- 2 * (gs * z1 - z2)
    gg[tn1] <- gg[tn1] - 4 * gs * x[tn1] * z1
    return(gg)
}

genrose.h <- function(x, gs=NULL) { ## compute Hessian
   if(is.null(gs)) { gs=100.0 }
    n <- length(x)
    hh<-matrix(rep(0, n*n),n,n)
    for (i in 2:n) {
        z1<-x[i]-x[i-1]*x[i-1]
#        z2<-1.0-x[i]
                hh[i,i]<-hh[i,i]+2.0*(gs+1.0)
                hh[i-1,i-1]<-hh[i-1,i-1]-4.0*gs*z1-4.0*gs*x[i-1]*(-2.0*x[i-1])
                hh[i,i-1]<-hh[i,i-1]-4.0*gs*x[i-1]
                hh[i-1,i]<-hh[i-1,i]-4.0*gs*x[i-1]
    }
        return(hh)
}

trad<-c(-1.2,1)
fval<-genrose.f(trad)
gval<-genrose.g(trad)
Ahess<-genrose.h(trad)
cat("Traditional start\n")
print(trad)
cat("f, g, H\n")
print(fval)
print(gval)
print(Ahess)
cat("\n\n By ufn etc.\n")

myfn<-list2env(list(fn=genrose.f, gr=genrose.g, hess=genrose.h, MAXIMIZE=FALSE, PARSCALE=rep(1,npar), FNSCALE=1,
       KFN=0, KGR=0, KHESS=0))


uf<-ufn(trad, fnuser=myfn)
ugH<-ugHgenb(trad, fnuser=myfn, control=list(ktrace=2))
print(uf)
print(ugH)
cat("Comparisons\n")
cat("Gradient max abs difference: ", max(abs(gval-ugH$gn)),"\n")
rh<-jacobian(grr, trad)
cat("Hessiant max abs difference: ", max(abs(Ahess-ugH$Hn)),"\n")
cat("\n\n")
rm(myfn)

tmp<-readline("Try alternative genrosa for npar=2 Rosenbrock")
fvala<-genrosa.f(trad)
gvala<-genrosa.g(trad)
Ahessa<-genrosa.h(trad)
cat("Traditional start\n")
print(trad)
npar<-length(trad)
cat("Alt f, g, H\n")
print(fvala)
print(gvala)
print(Ahessa)
cat("\n\n By ufn etc.\n")
myfna<-list2env(list(fn=genrosa.f, gr=genrosa.g, hess=genrosa.h, MAXIMIZE=FALSE, PARSCALE=rep(1,npar), FNSCALE=1,
       KFN=0, KGR=0, KHESS=0))
ufa<-ufn(trad, fnuser=myfna)
ugHa<-ugHgenb(trad, fnuser=myfna)
print(ufa)
print(ugHa)
gna<-grad(genrosa.f, trad)
hna<-hessian(genrose.f, trad)
rh<-jacobian(grr, trad)
cat("rh:")
print(rh)
cat("numeric grad\n")
print(gna)
cat("numeric hessian\n")
print(hna)
cat("Comparisons\n")
cat("Gradient max abs difference: ", max(abs(gvala-ugHa$gn)),"\n")
cat("Hessiant max abs difference: ", max(abs(Ahessa-ugHa$Hn)),"\n")
cat("\n\n")
rm(myfna)

tmp<-readline("genrose trad start, but gs=1")
trad<-c(-1.2,1)
fval<-genrosa.f(trad, gs=1)
gval<-genrosa.g(trad, gs=1)
Ahess<-genrosa.h(trad, gs=1)

myfna<-list2env(list(fn=genrosa.f, gr=genrosa.g, hess=genrosa.h, MAXIMIZE=FALSE, PARSCALE=rep(1,npar), FNSCALE=1,
       KFN=0, KGR=0, KHESS=0, dots=list(gs=1)))
cat("Traditional start\n")
print(trad)
cat("f, g, H\n")
print(fval)
print(gval)
print(Ahess)
gennog<-ugHgenb(trad,fnuser=myfna)
cat("results of ugHgenb for genrosa at \n")
print(trad)
print(gennog)
cat("Comparisons\n")
cat("Gradient max abs difference: ", max(abs(gval-gennog$gn)),"\n")
rh<-jacobian(grr, trad)
cat("Hessiant max abs difference: ", max(abs(Ahess-gennog$Hn)),"\n")
cat("\n\n")
rm(myfna)

tmp<-readline("now try higher dimension and different start")

parx<-rep(1,4)
npar<-length(parx)
lower<-rep(-10,4)
upper<-rep(10,4)
fval<-genrose.f(parx)
gval<-genrose.g(parx)
Ahess<-genrose.h(parx)

myfn<-list2env(list(fn=genrose.f, gr=genrose.g, hess=genrose.h, MAXIMIZE=FALSE, PARSCALE=rep(1,npar), FNSCALE=1,
       KFN=0, KGR=0, KHESS=0))
gennog<-ugHgenb(parx,fnuser=myfn, control=list(ktrace=1))
cat("results of ugHgenb for genrose without gradient code at \n")
print(parx)
print(gennog)
cat("compare to g =")
print(gval)
cat("and Hess\n")
print(Ahess)
cat("Comparisons\n")
cat("Gradient max abs difference: ", max(abs(gval-gennog$gn)),"\n")
rh<-jacobian(grr, trad)
cat("Hessiant max abs difference: ", max(abs(Ahess-gennog$Hn)),"\n")
cat("*****************************************\n")
cat("\n\n")
rm(myfn)

tmp<-readline("try with hessian set to NULL")

myfn2<-list2env(list(fn=genrose.f, gr=genrose.g, hess=NULL, MAXIMIZE=FALSE, PARSCALE=rep(1,npar), FNSCALE=1,
       KFN=0, KGR=0, KHESS=0))
geng<-ugHgenb(parx,fnuser=myfn2, control=list(ktrace=1))
cat("results of ugHgenb for genrose at ")
print(parx)
print(geng)
cat("compare to g =")
print(gval)
cat("and Hess\n")
print(Ahess)
cat("Comparisons\n")
cat("Gradient max abs difference: ", max(abs(gval-geng$gn)),"\n")
rh<-jacobian(grr, trad)
cat("Hessiant max abs difference: ", max(abs(Ahess-geng$Hn)),"\n")
cat("*****************************************\n")
cat("\n\n")
rm(myfn2)

tmp<-readline("try from all parameters 0.9, gs=9.4")

parx<-rep(0.9,4)
print(parx)
fval<-genrose.f(parx, gs=9.4)
cat("fn = ",fval,"\n")
gval<-genrose.g(parx, gs=9.4)
cat("g =")
print(gval)
Ahess<-genrose.h(parx, gs=9.4)
cat("Hess =\n")
print(Ahess)

myfn3<-list2env(list(fn=genrose.f, gr=genrose.g, hess=NULL, MAXIMIZE=FALSE, PARSCALE=rep(1,npar), FNSCALE=1,
       KFN=0, KGR=0, KHESS=0, dots=list(gs=9.4)))

gennog<-ugHgenb(parx,fnuser=myfn3, control=list(ktrace=1))

cat("results of ugHgenb with gs=",9.4," for genrose without gradient or Hessian code \n")
print(gennog)
cat("Comparisons\n")
cat("Gradient max abs difference: ", max(abs(gval-gennog$gn)),"\n")
cat("Hessiant max abs difference: ", max(abs(Ahess-gennog$Hn)),"\n")
cat("*****************************************\n")
cat("\n\n")
rm(myfn3)

tmp<-readline("Change gs to 5")
myfn4<-list2env(list(fn=genrose.f, gr=genrose.g, hess=NULL, MAXIMIZE=FALSE, PARSCALE=rep(1,npar), FNSCALE=1,
       KFN=0, KGR=0, KHESS=0, dots=list(gs=5)))

cat("\n\nTest with masks and gs=",5,"\n")
msk<-c(1,1,0,1) # masked parameter 3

gengb<-ugHgenb(parx,fnuser=myfn4, bdmsk=msk, control=list(ktrace=1))
print(gengb)
cat("*****************************************\n")

rm(myfn4)

optfntools documentation built on May 2, 2019, 4:26 p.m.