powell_bs: Powell Badly Scaled Function

In jlmelville/funconstrain: Functions for Testing Unconstrained Numerical Optimization

Powell Badly Scaled Function

Description

Test function 3 from the More', Garbow and Hillstrom paper.

Usage

powell_bs()

Details

The objective function is the sum of m functions, each of n parameters.

• Dimensions: Number of parameters n = 2, number of summand functions m = 2.

• Minima: f = 0 at (1.098... e-5, 9.106...) ,

Value

A list containing:

• fn Objective function which calculates the value given input parameter vector.

• gr Gradient function which calculates the gradient vector given input parameter vector.

• he If available, the hessian matrix (second derivatives) of the function w.r.t. the parameters at the given values.

• fg A function which, given the parameter vector, calculates both the objective value and gradient, returning a list with members fn and gr, respectively.

• x0 Standard starting point.

• fmin reported minimum

• xmin parameters at reported minimum

References

More', J. J., Garbow, B. S., & Hillstrom, K. E. (1981). Testing unconstrained optimization software. ACM Transactions on Mathematical Software (TOMS), 7(1), 17-41. \Sexpr[results=rd]{tools:::Rd_expr_doi("doi.org/10.1145/355934.355936")}

Powell, M. J. D. (1970). A hybrid method for nonlinear equations. In P. Rabinowitz (Ed.), Numerical Methods for Nonlinear Algebraic Equations (pp87-114). New York: Gordon & Breach.

Examples

fun <- powell_bs()
# Optimize using the standard starting point
x0 <- fun$x0
res_x0 <- stats::optim(par = x0, fn = fun$fn, gr = fun$gr, method =
"L-BFGS-B")
# Use your own starting point
res <- stats::optim(c(0.1, 0.2), fun$fn, fun$gr, method = "L-BFGS-B")

jlmelville/funconstrain documentation built on April 17, 2024, 7:47 p.m.