| sane {BB} | R Documentation |
Non-Monotone spectral approach for Solving Large-Scale Nonlinear Systems of Equations
sane(par, fn, method=1, control=list(), ...)
fn |
a function that takes a real vector as argument and returns a real vector of same length (see details). |
par |
A real vector argument to fn, indicating the
initial guess for the root of the nonlinear system. |
method |
An integer (1, 2, or 3) specifying which Barzilai-Borwein steplength to use. The default is 1. See *Details*. |
control |
A list of control parameters. See *Details*. |
... |
Additional arguments passed to fn. |
The function sane implements a non-monotone spectral residual method for finding a root of nonlinear systems. It stands for "spectral approach for nonlinear equations".
It differs from the function dfsane in that it requires an approximation of a directional derivative at every iteration of the merit function $F(x)^t F(x)$.
R adaptation, with significant modifications, by Ravi Varadhan, Johns Hopkins University (March 25, 2008), from the original FORTRAN code of La Cruz and Raydan (2003) available at http://kuainasi.ciens.ucv.ve/ccct/mraydan/mraydan.html. .
A major modification in our R adaptation of the original FORTRAN code is the availability of 3 different options for Barzilai-Borwein (BB) steplengths: method = 1 is the BB
steplength used in LaCruz and Raydan (2003); method = 2 is equivalent to the other steplength proposed in Barzilai and Borwein's (1988) original paper.
Finally, method = 3, is a new steplength, which is equivalent to that first proposed in Varadhan and Roland (2008) for accelerating the EM algorithm.
In fact, Varadhan and Roland (2008) considered 3 equivalent steplength schemes in their EM acceleration work. Here, we have chosen method = 1
as the "default" method. However, we have not seen major differences between the three steplength schemes in our experiments.
Argument control is a list specifing any changes to default values of
algorithm control parameters. Note that the names of these must be
specified completely. Partial matching will not work.
M=1 would enforce strict monotonicity
in the reduction of L2-norm of fn, whereas larger values allow for more non-monotonicity. Global convergence under non-monotonicity is ensured by
enforcing the Grippo-Lampariello-Lucidi condition (Grippo et al. 1986) in a non-monotone line-search algorithm. Values of M between 5 to 20 are generally good. The default is M = 10.maxit = 1500.tol = 1.e-07.TRUE, information on the progress of solving the system is produced.
Default is trace = TRUE.trace=TRUE. Default is triter=10, which means that
the L2-norm of fn is printed at every 10-th iteration.A list with the following components:
par |
The best set of parameters that solves the nonlinear system. |
residual |
L2-norm of the function evaluated at par, divided
by sqrt(npar), where npar is the number of parameters. |
fn.reduction |
Reduction in the L2-norm of the function from the initial L2-norm. |
feval |
Number of times fn was evaluated. |
iter |
Number of iterations taken by the algorithm. |
convergence |
An integer code indicating type of convergence. 0
indicates successful convergence, in which case the resid is smaller
than tol Error codes are 1 indicates that the iteration
limit maxit has been reached. 2 indicates failure due to an
error in function evaluation. 3 indicates failure due to
exceeding 100 steplength reductions in line-search. 4 indicates
failure due to an anomalous iteration. |
message |
A text message explaining which termination criterion was used. |
J Barzilai, and JM Borwein (1988), Two-point step size gradient methods, IMA J Numerical Analysis, 8, 141-148.
L Grippo, F Lampariello, and S Lucidi (1986), A nonmonotone line search technique for Newton's method, SIAM J on Numerical Analysis, 23, 707-716.
W LaCruz, and M Raydan (2003), Nonmonotone spectral methods for large-scale nonlinear systems, Optimization Methods and Software, 18, 583-599 (see http://kuainasi.ciens.ucv.ve/ccct/mraydan/mraydan.html).
R Varadhan and C Roland (2008), Simple and globally-convergent methods for accelerating the convergence of any EM algorithm, Scandinavian J Statistics, doi: 10.1111/j.1467-9469.2007.00585.x.
R Varadhan and PD Gilbert (2008), BB: An R package of Barzilai-Borwein spectral methods for solving and optimizing large-scale nonlinear systems, Unpublished.
trigexp <- function(x) {
# Test function No. 12 in the Appendix of LaCruz and Raydan (2003)
n <- length(x)
F <- rep(NA, n)
F[1] <- 3*x[1]^2 + 2*x[2] - 5 + sin(x[1] - x[2]) * sin(x[1] + x[2])
tn1 <- 2:(n-1)
F[tn1] <- -x[tn1-1] * exp(x[tn1-1] - x[tn1]) + x[tn1] * ( 4 + 3*x[tn1]^2) +
2 * x[tn1 + 1] + sin(x[tn1] - x[tn1 + 1]) * sin(x[tn1] + x[tn1 + 1]) - 8
F[n] <- -x[n-1] * exp(x[n-1] - x[n]) + 4*x[n] - 3
F
}
p0 <- rnorm(1000)
sane(par=p0, fn=trigexp)
sane(par=p0, fn=trigexp, method=2)
sane(par=p0, fn=trigexp, control=list(triter=5, M=20))