Package: ucminf 1.2.2

K Hervé Dakpo

ucminf: General-Purpose Unconstrained Non-Linear Optimization

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'.

Authors:K Hervé Dakpo [ctb, cre], Hans Bruun Nielsen [aut], Stig Bousgaard Mortensen [aut]

ucminf_1.2.2.tar.gz
ucminf_1.2.2.tar.gz(r-4.5-noble)ucminf_1.2.2.tar.gz(r-4.4-noble)
ucminf_1.2.2.tgz(r-4.4-emscripten)ucminf_1.2.2.tgz(r-4.3-emscripten)
ucminf.pdf |ucminf.html✨
ucminf/json (API)
NEWS

# Install 'ucminf' in R:
install.packages('ucminf', repos = 'https://cloud.r-project.org')

Bug tracker:https://github.com/hdakpo/ucminf/issues0 issues

Uses libs:
  • openblas– Optimized BLAS

On CRAN:

Conda:

fortranopenblas

6.56 score 204 packages 59k downloads 2 mentions 1 exports 0 dependencies

Last updated 9 months agofrom:8f8ab37713. Checks:3 OK. Indexed: no.

TargetResultLatest binary
Doc / VignettesOKMar 04 2025
R-4.5-linux-x86_64OKMar 04 2025
R-4.4-linux-x86_64OKMar 04 2025

Exports:ucminf

Dependencies:

Citation

To cite package ‘ucminf’ in publications use:

Nielsen H, Mortensen S (2024). ucminf: General-Purpose Unconstrained Non-Linear Optimization. R package version 1.2.2, https://CRAN.R-project.org/package=ucminf.

Corresponding BibTeX entry: 

  @Manual{,
    title = {ucminf: General-Purpose Unconstrained Non-Linear
      Optimization},
    author = {Hans Bruun Nielsen and Stig Bousgaard Mortensen},
    year = {2024},
    note = {R package version 1.2.2},
    url = {https://CRAN.R-project.org/package=ucminf},
  }

Readme and manuals

ucminf

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

Installation

You can install the development version of ucminf from GitHub with:

# install.packages("devtools")
devtools::install_github("hdakpo/ucminf")

Example

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+01