# gHgen: Generate gradient and Hessian for a function at given... In optextras: Tools to Support Optimization Possibly with Bounds and Masks

## Description

`gHgen` 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, otherwise package `numDeriv` is used. If a user-provided Hessian function `hess` is available, it is used to compute a Hessian. Otherwise, if `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

 ```1 2``` ``` gHgen(par, fn, gr=NULL, hess=NULL, control=list(ktrace=0), ...) ```

## Arguments

 `par` Set of parameters, assumed to be at a minimum of the function `fn`. `fn` Name of the objective function. `gr` (Optional) function to compute the gradient of the objective function. If present, we use the Jacobian of the gradient as the Hessian and avoid one layer of numerical approximation to the Hessian. `hess` (Optional) function to compute the Hessian of the objective function. This is rarely available, but is included for completeness. `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, a logical flag which, if TRUE, monitors the progress of gHgen (default FALSE), and stoponerror, defaulting to FALSE to NOT stop when there is an error or asymmetry of Hessian. Set TRUE to stop. `...` Extra data needed to compute the function, gradient and Hessian.

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` Always 0. The number of active bounds and masks. Present to make function consistent with `gHgenb`.

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56``` ```# 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) ans100fgh<- gHgen(trad, genrose.f, gr=genrose.g, hess=genrose.h, control=list(ktrace=1)) print(ans100fgh) ans100fg<- gHgen(trad, genrose.f, gr=genrose.g, control=list(ktrace=1)) print(ans100fg) ans100f<- gHgen(trad, genrose.f, control=list(ktrace=1)) print(ans100f) ans10fgh<- gHgen(trad, genrose.f, gr=genrose.g, hess=genrose.h, control=list(ktrace=1), gs=10) print(ans10fgh) ans10fg<- gHgen(trad, genrose.f, gr=genrose.g, control=list(ktrace=1), gs=10) print(ans10fg) ans10f<- gHgen(trad, genrose.f, control=list(ktrace=1), gs=10) print(ans10f) ```

optextras documentation built on Dec. 31, 2019, 3:01 a.m.