stogo | R Documentation |
StoGO is a global optimization algorithm that works by systematically dividing the search space into smaller hyper-rectangles.
stogo(
x0,
fn,
gr = NULL,
lower = NULL,
upper = NULL,
maxeval = 10000,
xtol_rel = 1e-06,
randomized = FALSE,
nl.info = FALSE,
...
)
x0 |
initial point for searching the optimum. |
fn |
objective function that is to be minimized. |
gr |
optional gradient of the objective function. |
lower , upper |
lower and upper bound constraints. |
maxeval |
maximum number of function evaluations. |
xtol_rel |
stopping criterion for relative change reached. |
randomized |
logical; shall a randomizing variant be used? |
nl.info |
logical; shall the original NLopt info been shown. |
... |
additional arguments passed to the function. |
StoGO is a global optimization algorithm that works by systematically dividing the search space (which must be bound-constrained) into smaller hyper-rectangles via a branch-and-bound technique, and searching them by a gradient-based local-search algorithm (a BFGS variant), optionally including some randomness.
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 (> 0) or a possible error number (< 0). |
message |
character string produced by NLopt and giving additional information. |
Only bound-constrained problems are supported by this algorithm.
Hans W. Borchers
S. Zertchaninov and K. Madsen, “A C++ Programme for Global Optimization,” IMM-REP-1998-04, Department of Mathematical Modelling, Technical University of Denmark.
### Rosenbrock Banana objective function
fn <- function(x)
return( 100 * (x[2] - x[1] * x[1])^2 + (1 - x[1])^2 )
x0 <- c( -1.2, 1 )
lb <- c( -3, -3 )
ub <- c( 3, 3 )
stogo(x0 = x0, fn = fn, lower = lb, upper = ub)
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