The hardware and bandwidth for this mirror is donated by METANET, the Webhosting and Full Service-Cloud Provider.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]metanet.ch.
The goal of ucminf is to provide an algorithm for
general-purpose unconstrained non-linear optimization. The algorithm is
of quasi-Newton type with BFGS updating of the inverse Hessian and soft
line search with a trust region type monitoring of the input to the line
search algorithm. The interface of ucminf is designed for easy
interchange with optim
You can install the development version of ucminf from GitHub with:
# install.packages("devtools")
devtools::install_github("hdakpo/ucminf")library(ucminf)
# Rosenbrock Banana function
fR <- function(x) (1 - x[1])^2 + 100 * (x[2] - x[1]^2)^2
gR <- function(x) c(-400 * x[1] * (x[2] - x[1] * x[1]) - 2 * (1 - x[1]),
200 * (x[2] - x[1] * x[1]))
## Find minimum and show trace
optRes <- ucminf(par = c(2,.5), fn = fR, gr = gR, control = list(trace = 1))
#> neval = 1, F(x) = 1.2260e+03, max|g(x)| = 2.8020e+03
#> x = 2.0000e+00, 5.0000e-01
#> Line search: alpha = 1.0000e+00, dphi(0) =-2.8881e+03, dphi(1) =-1.4263e+02
#> neval = 2, F(x) = 1.0123e+01, max|g(x)| = 1.3111e+02
#> x = 1.0298e+00, 7.4237e-01
#> Line search: alpha = 1.0000e+00, dphi(0) =-3.1743e+01, dphi(1) = 1.0180e+01
#> neval = 3, F(x) = 1.7049e+00, max|g(x)| = 6.3969e+01
#> x = 1.2600e+00, 1.7155e+00
#> Line search: alpha = 1.0000e+00, dphi(0) =-2.5788e+00, dphi(1) =-5.6182e-01
#> neval = 4, F(x) = 1.1612e-01, max|g(x)| = 1.2343e+01
#> x = 1.2174e+00, 1.5083e+00
#> Line search: alpha = 1.0000e+00, dphi(0) =-1.5867e-01, dphi(1) = 1.2108e-02
#> neval = 5, F(x) = 4.2253e-02, max|g(x)| = 1.8638e+00
#> x = 1.2033e+00, 1.4449e+00
#> Line search: alpha = 1.0000e+00, dphi(0) =-1.1826e-03, dphi(1) =-3.2371e-04
#> neval = 6, F(x) = 4.1500e-02, max|g(x)| = 8.6681e-01
#> x = 1.2035e+00, 1.4474e+00
#> Line search: alpha = 1.0000e+00, dphi(0) =-5.9673e-04, dphi(1) =-4.7194e-04
#> neval = 7, F(x) = 4.0965e-02, max|g(x)| = 4.8839e-01
#> x = 1.2024e+00, 1.4456e+00
#> Line search: alpha = 1.0000e+00, dphi(0) =-3.9731e-03, dphi(1) =-2.3018e-03
#> neval = 8, F(x) = 3.7853e-02, max|g(x)| = 8.5215e-01
#> x = 1.1928e+00, 1.4254e+00
#> Line search: alpha = 1.0000e+00, dphi(0) =-8.0453e-03, dphi(1) =-6.3954e-03
#> neval = 9, F(x) = 3.0800e-02, max|g(x)| = 2.0990e+00
#> x = 1.1676e+00, 1.3685e+00
#> Line search: alpha = 8.2084e-01, dphi(0) =-4.4175e-02, dphi(1) = 1.8746e-02
#> neval = 11, F(x) = 4.8486e-03, max|g(x)| = 2.2862e+00
#> x = 1.0458e+00, 1.0884e+00
#> Line search: alpha = 3.8293e-01, dphi(0) =-4.8734e-03, dphi(1) = 4.6817e-04
#> neval = 13, F(x) = 4.0485e-03, max|g(x)| = 1.1863e+00
#> x = 1.0584e+00, 1.1177e+00
#> Line search: alpha = 1.0000e+00, dphi(0) =-6.4354e-04, dphi(1) =-5.6879e-04
#> neval = 14, F(x) = 3.4426e-03, max|g(x)| = 1.1238e+00
#> x = 1.0535e+00, 1.1074e+00
#> Line search: alpha = 1.0000e+00, dphi(0) =-4.7371e-03, dphi(1) =-1.0920e-03
#> neval = 15, F(x) = 6.1678e-04, max|g(x)| = 7.3075e-01
#> x = 1.0180e+00, 1.0347e+00
#> Line search: alpha = 1.0000e+00, dphi(0) =-7.9043e-04, dphi(1) =-2.5377e-04
#> neval = 16, F(x) = 1.0437e-04, max|g(x)| = 1.6394e-01
#> x = 1.0096e+00, 1.0189e+00
#> Line search: alpha = 1.0000e+00, dphi(0) =-1.8089e-04, dphi(1) =-1.8237e-05
#> neval = 17, F(x) = 5.8219e-06, max|g(x)| = 9.1455e-02
#> x = 1.0009e+00, 1.0016e+00
#> Line search: alpha = 1.0000e+00, dphi(0) =-1.3102e-05, dphi(1) = 2.0222e-06
#> neval = 18, F(x) = 2.9162e-07, max|g(x)| = 1.7185e-02
#> x = 1.0003e+00, 1.0007e+00
#> Line search: alpha = 1.0000e+00, dphi(0) =-5.9332e-07, dphi(1) = 1.1234e-08
#> neval = 19, F(x) = 1.2578e-10, max|g(x)| = 2.0751e-04
#> x = 9.9999e-01, 9.9998e-01
#> Line search: alpha = 1.0000e+00, dphi(0) =-2.5270e-10, dphi(1) = 1.1297e-12
#> neval = 20, F(x) = 3.5670e-15, max|g(x)| = 2.0836e-06
#> x = 1.0000e+00, 1.0000e+00
#> Line search: alpha = 1.0000e+00, dphi(0) =-7.1150e-15, dphi(1) =-1.8980e-17
#> Optimization has converged
#> Stopped by small gradient (grtol).
#> maxgradient laststep stepmax neval
#> 1.020598e-08 6.480989e-08 1.225000e-01 2.100000e+01These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.