bard | R Documentation |
Test function 8 from the More', Garbow and Hillstrom paper.
bard()
The objective function is the sum of m
functions, each of n
parameters.
Dimensions: Number of parameters n = 3
, number of summand
functions m = 15
.
Minima: f = 8.214877e-3
at c(0.08241056, 1.133036, 2.343695)
Solvers terminate with f
near 17 for parameter 1 in 0.84 to 0.89 approximately
and large negative values of the other two parameters.
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
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")}
Bard, Y. (1970). Comparison of gradient methods for the solution of nonlinear parameter estimation problems. SIAM Journal on Numerical Analysis, 7(1), 157-186. \Sexpr[results=rd]{tools:::Rd_expr_doi("dx.doi.org/10.1137/0707011")}
fun <- bard()
# Optimize using the standard starting point
x0 <- fun$x0
res_x0 <- stats::optim(par = x0, fn = fun$fn, gr = fun$gr, method =
"L-BFGS-B")
# Use your own starting point
res <- stats::optim(c(0.1, 0.2, 0.3), fun$fn, fun$gr, method = "L-BFGS-B")
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