optim

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General-purpose Optimization

General-purpose optimization based on Nelder--Mead, quasi-Newton and conjugate-gradient algorithms. It includes an option for box-constrained optimization and simulated annealing.

Keywords
nonlinear, optimize
Usage
optim(par, fn, gr = NULL, ...,
      method = c("Nelder-Mead", "BFGS", "CG", "L-BFGS-B", "SANN",
                 "Brent"),
      lower = -Inf, upper = Inf,
      control = list(), hessian = FALSE)

optimHess(par, fn, gr = NULL, ..., control = list())

Arguments
par
Initial values for the parameters to be optimized over.
fn
A function to be minimized (or maximized), with first argument the vector of parameters over which minimization is to take place. It should return a scalar result.
gr
A function to return the gradient for the "BFGS", "CG" and "L-BFGS-B" methods. If it is NULL, a finite-difference approximation will be used.

For the "SANN" method it specifies a function to generate a new candidate point. If it is NULL a default Gaussian Markov kernel is used.

...
Further arguments to be passed to fn and gr.
method
The method to be used. See Details. Can be abbreviated.
lower, upper
Bounds on the variables for the "L-BFGS-B" method, or bounds in which to search for method "Brent".
control
A list of control parameters. See Details.
hessian
Logical. Should a numerically differentiated Hessian matrix be returned?
Details

Note that arguments after ... must be matched exactly.

By default optim performs minimization, but it will maximize if control$fnscale is negative. optimHess is an auxiliary function to compute the Hessian at a later stage if hessian = TRUE was forgotten.

The default method is an implementation of that of Nelder and Mead (1965), that uses only function values and is robust but relatively slow. It will work reasonably well for non-differentiable functions.

Method "BFGS" is a quasi-Newton method (also known as a variable metric algorithm), specifically that published simultaneously in 1970 by Broyden, Fletcher, Goldfarb and Shanno. This uses function values and gradients to build up a picture of the surface to be optimized.

Method "CG" is a conjugate gradients method based on that by Fletcher and Reeves (1964) (but with the option of Polak--Ribiere or Beale--Sorenson updates). Conjugate gradient methods will generally be more fragile than the BFGS method, but as they do not store a matrix they may be successful in much larger optimization problems.

Method "L-BFGS-B" is that of Byrd et. al. (1995) which allows box constraints, that is each variable can be given a lower and/or upper bound. The initial value must satisfy the constraints. This uses a limited-memory modification of the BFGS quasi-Newton method. If non-trivial bounds are supplied, this method will be selected, with a warning.

Nocedal and Wright (1999) is a comprehensive reference for the previous three methods.

Method "SANN" is by default a variant of simulated annealing given in Belisle (1992). Simulated-annealing belongs to the class of stochastic global optimization methods. It uses only function values but is relatively slow. It will also work for non-differentiable functions. This implementation uses the Metropolis function for the acceptance probability. By default the next candidate point is generated from a Gaussian Markov kernel with scale proportional to the actual temperature. If a function to generate a new candidate point is given, method "SANN" can also be used to solve combinatorial optimization problems. Temperatures are decreased according to the logarithmic cooling schedule as given in Belisle (1992, p.890); specifically, the temperature is set to temp / log(((t-1) %/% tmax)*tmax + exp(1)), where t is the current iteration step and temp and tmax are specifiable via control, see below. Note that the "SANN" method depends critically on the settings of the control parameters. It is not a general-purpose method but can be very useful in getting to a good value on a very rough surface.

Method "Brent" is for one-dimensional problems only, using optimize(). It can be useful in cases where optim() is used inside other functions where only method can be specified, such as in mle from package stats4.

Function fn can return NA or Inf if the function cannot be evaluated at the supplied value, but the initial value must have a computable finite value of fn. (Except for method "L-BFGS-B" where the values should always be finite.)

optim can be used recursively, and for a single parameter as well as many. It also accepts a zero-length par, and just evaluates the function with that argument.

The control argument is a list that can supply any of the following components: [object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Any names given to par will be copied to the vectors passed to fn and gr. Note that no other attributes of par are copied over.

The parameter vector passed to fn has special semantics and may be shared between calls: the function should not change or copy it.

Value

  • For optim, a list with components:
  • parThe best set of parameters found.
  • valueThe value of fn corresponding to par.
  • countsA two-element integer vector giving the number of calls to fn and gr respectively. This excludes those calls needed to compute the Hessian, if requested, and any calls to fn to compute a finite-difference approximation to the gradient.
  • convergenceAn integer code. 0 indicates successful completion (which is always the case for "SANN" and "Brent"). Possible error codes are [object Object],[object Object],[object Object],[object Object]
  • messageA character string giving any additional information returned by the optimizer, or NULL.
  • hessianOnly if argument hessian is true. A symmetric matrix giving an estimate of the Hessian at the solution found. Note that this is the Hessian of the unconstrained problem even if the box constraints are active.
  • For optimHess, the description of the hessian component applies.

Note

optim will work with one-dimensional pars, but the default method does not work well (and will warn). Method "Brent" uses optimize and needs bounds to be available; "BFGS" often works well enough if not.

concept

  • minimization
  • maximization

source

The code for methods "Nelder-Mead", "BFGS" and "CG" was based originally on Pascal code in Nash (1990) that was translated by p2c and then hand-optimized. Dr Nash has agreed that the code can be made freely available.

The code for method "L-BFGS-B" is based on Fortran code by Zhu, Byrd, Lu-Chen and Nocedal obtained from Netlib (file opt/lbfgs_bcm.shar: another version is in toms/778).

The code for method "SANN" was contributed by A. Trapletti.

References

Belisle, C. J. P. (1992) Convergence theorems for a class of simulated annealing algorithms on $R^d$. J. Applied Probability, 29, 885--895.

Byrd, R. H., Lu, P., Nocedal, J. and Zhu, C. (1995) A limited memory algorithm for bound constrained optimization. SIAM J. Scientific Computing, 16, 1190--1208.

Fletcher, R. and Reeves, C. M. (1964) Function minimization by conjugate gradients. Computer Journal 7, 148--154.

Nash, J. C. (1990) Compact Numerical Methods for Computers. Linear Algebra and Function Minimisation. Adam Hilger.

Nelder, J. A. and Mead, R. (1965) A simplex algorithm for function minimization. Computer Journal 7, 308--313.

Nocedal, J. and Wright, S. J. (1999) Numerical Optimization. Springer.

See Also

nlm, nlminb.

optimize for one-dimensional minimization and constrOptim for constrained optimization.

Aliases
  • optim
  • optimHess
Examples
library(stats) require(graphics) 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)) } optim(c(-1.2,1), fr) (res <- optim(c(-1.2,1), fr, grr, method = "BFGS")) optimHess(res$par, fr, grr) optim(c(-1.2,1), fr, NULL, method = "BFGS", hessian = TRUE) ## These do not converge in the default number of steps optim(c(-1.2,1), fr, grr, method = "CG") optim(c(-1.2,1), fr, grr, method = "CG", control = list(type = 2)) optim(c(-1.2,1), fr, grr, method = "L-BFGS-B") flb <- function(x) { p <- length(x); sum(c(1, rep(4, p-1)) * (x - c(1, x[-p])^2)^2) } ## 25-dimensional box constrained optim(rep(3, 25), flb, NULL, method = "L-BFGS-B", lower = rep(2, 25), upper = rep(4, 25)) # par[24] is *not* at boundary ## "wild" function , global minimum at about -15.81515 fw <- function (x) 10*sin(0.3*x)*sin(1.3*x^2) + 0.00001*x^4 + 0.2*x+80 plot(fw, -50, 50, n = 1000, main = "optim() minimising 'wild function'") res <- optim(50, fw, method = "SANN", control = list(maxit = 20000, temp = 20, parscale = 20)) res ## Now improve locally {typically only by a small bit}: (r2 <- optim(res$par, fw, method = "BFGS")) points(r2$par, r2$value, pch = 8, col = "red", cex = 2) ## Combinatorial optimization: Traveling salesman problem library(stats) # normally loaded eurodistmat <- as.matrix(eurodist) distance <- function(sq) { # Target function sq2 <- embed(sq, 2) sum(eurodistmat[cbind(sq2[,2], sq2[,1])]) } genseq <- function(sq) { # Generate new candidate sequence idx <- seq(2, NROW(eurodistmat)-1) changepoints <- sample(idx, size = 2, replace = FALSE) tmp <- sq[changepoints[1]] sq[changepoints[1]] <- sq[changepoints[2]] sq[changepoints[2]] <- tmp sq } sq <- c(1:nrow(eurodistmat), 1) # Initial sequence: alphabetic distance(sq) # rotate for conventional orientation loc <- -cmdscale(eurodist, add = TRUE)$points x <- loc[,1]; y <- loc[,2] s <- seq_len(nrow(eurodistmat)) tspinit <- loc[sq,] plot(x, y, type = "n", asp = 1, xlab = "", ylab = "", main = "initial solution of traveling salesman problem", axes = FALSE) arrows(tspinit[s,1], tspinit[s,2], tspinit[s+1,1], tspinit[s+1,2], angle = 10, col = "green") text(x, y, labels(eurodist), cex = 0.8) set.seed(123) # chosen to get a good soln relatively quickly res <- optim(sq, distance, genseq, method = "SANN", control = list(maxit = 30000, temp = 2000, trace = TRUE, REPORT = 500)) res # Near optimum distance around 12842 tspres <- loc[res$par,] plot(x, y, type = "n", asp = 1, xlab = "", ylab = "", main = "optim() 'solving' traveling salesman problem", axes = FALSE) arrows(tspres[s,1], tspres[s,2], tspres[s+1,1], tspres[s+1,2], angle = 10, col = "red") text(x, y, labels(eurodist), cex = 0.8)
Documentation reproduced from package stats, version 3.3, License: Part of R 3.3

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