Preconditioned Truncated Newton
Truncated Newton methods, also calledNewton-iterative methods, solve an approximating Newton system using a conjugate-gradient approach and are related to limited-memory BFGS.
tnewton(x0, fn, gr = NULL, lower = NULL, upper = NULL, precond = TRUE, restart = TRUE, nl.info = FALSE, control = list(), ...)
x0 |
starting point for searching the optimum. |
fn |
objective function that is to be minimized. |
gr |
gradient of function |
lower, upper |
lower and upper bound constraints. |
precond |
logical; preset L-BFGS with steepest descent. |
restart |
logical; restarting L-BFGS with steepest descent. |
nl.info |
logical; shall the original NLopt info been shown. |
control |
list of options, see |
... |
additional arguments passed to the function. |
Truncated Newton methods are based on approximating the objective with a quadratic function and applying an iterative scheme such as the linear conjugate-gradient algorithm.
List with components:
par |
the optimal solution found so far. |
value |
the function value corresponding to |
iter |
number of (outer) iterations, see |
convergence |
integer code indicating successful completion (> 1) or a possible error number (< 0). |
message |
character string produced by NLopt and giving additional information. |
Less reliable than Newton's method, but can handle very large problems.
Hans W. Borchers
R. S. Dembo and T. Steihaug, “Truncated Newton algorithms for large-scale optimization,” Math. Programming 26, p. 190-212 (1982).
flb <- function(x) { p <- length(x) sum(c(1, rep(4, p-1)) * (x - c(1, x[-p])^2)^2) } # 25-dimensional box constrained: par[24] is *not* at boundary S <- tnewton(rep(3, 25), flb, lower=rep(2, 25), upper=rep(4, 25), nl.info = TRUE, control = list(xtol_rel=1e-8)) ## Optimal value of objective function: 368.105912874334 ## Optimal value of controls: 2 ... 2 2.109093 4
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