| Type: | Package |
| Title: | Non-Negative Conjugate-Gradient Minimizer |
| Version: | 0.1.6-1 |
| Author: | David Cortes |
| Maintainer: | David Cortes <david.cortes.rivera@gmail.com> |
| URL: | https://github.com/david-cortes/nonneg_cg |
| BugReports: | https://github.com/david-cortes/nonneg_cg/issues |
| Description: | Minimize a differentiable function subject to all the variables being non-negative (i.e. >= 0), using a Conjugate-Gradient algorithm based on a modified Polak-Ribiere-Polyak formula as described in (Li, Can, 2013, https://www.hindawi.com/journals/jam/2013/986317/abs/). |
| License: | BSD_2_clause + file LICENSE |
| Imports: | Rcpp (≥ 0.12.19) |
| LinkingTo: | Rcpp |
| RoxygenNote: | 6.1.1 |
| NeedsCompilation: | yes |
| Packaged: | 2021-09-25 23:51:23 UTC; david |
| Repository: | CRAN |
| Date/Publication: | 2021-09-26 04:10:08 UTC |
Non-Negative CG Minimizer
Description
Minimize a differentiable function subject to all the variables being non-negative (i.e. >= 0), using a Conjugate-Gradient algorithm based on a modified Polak-Ribiere-Polyak formula (see reference at the bottom for details).
Usage
minimize.nonneg.cg(evaluate_function, evaluate_gradient, x0, tol = 1e-04,
maxnfeval = 1500, maxiter = 200, decr_lnsrch = 0.5,
lnsrch_const = 0.01, max_ls = 20, extra_nonneg_tol = FALSE,
nthreads = 1, verbose = FALSE, ...)
Arguments
evaluate_function |
function(x, ...) objective evaluation function |
evaluate_gradient |
function(x, ...) gradient evaluation function |
x0 |
Starting point. Must be a feasible point (>=0). Be aware that it might be modified in-place. |
tol |
Tolerance for <gradient, direction> |
maxnfeval |
Maximum number of function evaluations |
maxiter |
Maximum number of CG iterations |
decr_lnsrch |
Number by which to decrease the step size after each unsuccessful line search |
lnsrch_const |
Acceptance parameter for the line search procedure |
max_ls |
Maximum number of line search trials per iteration |
extra_nonneg_tol |
Ensure extra non-negative tolerance by explicitly setting elements that are <=0 to zero at each iteration |
nthreads |
Number of parallel threads to use (ignored if the package was installed from CRAN) |
verbose |
Whether to print convergence messages |
... |
Extra parameters to pass to the objective and gradient functions |
Details
The underlying C function can also be called directly from Rcpp with 'R_GetCCallable' (see example of such usage in the source code of the 'zoo' package).
References
Li, C. (2013). A conjugate gradient type method for the nonnegative constraints optimization problems. Journal of Applied Mathematics, 2013.
Examples
fr <- function(x) { ## Rosenbrock Banana function
x1 <- x[1]
x2 <- x[2]
100 * (x2 - x1 * x1)^2 + (1 - x1)^2
}
grr <- function(x) { ## Gradient of 'fr'
x1 <- x[1]
x2 <- x[2]
c(-400 * x1 * (x2 - x1 * x1) - 2 * (1 - x1),
200 * (x2 - x1 * x1))
}
minimize.nonneg.cg(fr, grr, x0 = c(0,2), verbose=TRUE, tol=1e-8)