public class Bobyqa extends MultivariateOptimization
| Constructor and Description |
|---|
Bobyqa() |
| Modifier and Type | Method and Description |
|---|---|
static OptimizationResult |
bobyqa(double[] x,
double[] xl,
double[] xu,
MultivariableFunction calfun,
int npt,
double rhobeg,
double rhoend,
int maxfun,
boolean isMinimize)
This subroutine seeks the least value of a function of many variables,
by applying a trust region method that forms quadratic models by
interpolation.
|
static void |
main(java.lang.String[] args) |
OptimizationResult |
optimize(OptimizationConfig cfg) |
public OptimizationResult optimize(OptimizationConfig cfg)
optimize in class MultivariateOptimizationpublic static final OptimizationResult bobyqa(double[] x, double[] xl, double[] xu, MultivariableFunction calfun, int npt, double rhobeg, double rhoend, int maxfun, boolean isMinimize)
This subroutine seeks the least value of a function of many variables, by applying a trust region method that forms quadratic models by interpolation. There is usually some freedom in the interpolation conditions, which is taken up by minimizing the Frobenius norm of the change to the second derivative of the model, beginning with the zero matrix. The values of the variables are constrained by upper and lower bounds. The arguments of the subroutine are as follows. N must be set to the number of variables and must be at least two. NPT is the number of interpolation conditions. Its value must be in the interval [N+2,(N+1)(N+2)/2]. Choices that exceed 2*N+1 are not recommended. Initial values of the variables must be set in X(1),X(2),...,X(N). They will be changed to the values that give the least calculated F. For I=1,2,...,N, XL(I) and XU(I) must provide the lower and upper bounds, respectively, on X(I). The construction of quadratic models requires XL(I) to be strictly less than XU(I) for each I. Further, the contribution to a model from changes to the I-th variable is damaged severely by rounding errors if XU(I)-XL(I) is too small. RHOBEG and RHOEND must be set to the initial and final values of a trust region radius, so both must be positive with RHOEND no greater than RHOBEG. Typically, RHOBEG should be about one tenth of the greatest expected change to a variable, while RHOEND should indicate the accuracy that is required in the final values of the variables. An error return occurs if any of the differences XU(I)-XL(I), I=1,...,N, is less than 2*RHOBEG. The value of IPRINT should be set to 0, 1, 2 or 3, which controls the amount of printing. Specifically, there is no output if IPRINT=0 and there is output only at the return if IPRINT=1. Otherwise, each new value of RHO is printed, with the best vector of variables so far and the corresponding value of the objective function. Further, each new value of F with its variables are output if IPRINT=3. MAXFUN must be set to an upper bound on the number of calls of CALFUN. The array W will be used for working space. Its length must be at least (NPT+5)*(NPT+N)+3*N*(N+5)/2. DOUBLE PRECISION FUNCTION CALFUN (N,X,IP) has to be provided by the user. It returns the value of the objective function for the current values of the variables X(1),X(2),...,X(N), which are generated automatically in a way that satisfies the bounds given in XL and XU. Return if the value of NPT is unacceptable.
RJ's note: Storage bound [3.5*n^2 + 23.5*n + 14, (n^4 + 35*n^2)/4 + 2*n^3 + 24*n + 6]
x - Initial estimatexl - Lower estimatexu - Upper estimatecalfun - Function to optimizenpt - Number of interpolation pointsrhobeg - Initial trust region radiusrhoend - Final trust region radiusmaxfun - Maximum number of calls to calfunisMinimize - true if goal is to find the global minimumpublic static void main(java.lang.String[] args)