static double[] |
NumericalDerivative.diagonalHessian(MultivariateFunction f,
double[] x) |
determine diagonal of Hessian
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double |
MultivariateMinimum.findMinimum(MultivariateFunction f,
double[] xvec) |
Find minimum close to vector x
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double |
MultivariateMinimum.findMinimum(MultivariateFunction f,
double[] xvec,
int fxFracDigits,
int xFracDigits) |
Find minimum close to vector x
(desired fractional digits for each parameter is specified)
|
double |
MultivariateMinimum.findMinimum(MultivariateFunction f,
double[] xvec,
int fxFracDigits,
int xFracDigits,
MinimiserMonitor monitor) |
Find minimum close to vector x
(desired fractional digits for each parameter is specified)
|
static double[] |
NumericalDerivative.gradient(MultivariateFunction f,
double[] x) |
determine gradient
|
static void |
NumericalDerivative.gradient(MultivariateFunction f,
double[] x,
double[] grad) |
determine gradient
|
void |
MinimiserMonitor.newMinimum(double value,
double[] parameterValues,
MultivariateFunction beingOptimized) |
Inform monitor of a new minimum, along with the current arguments.
|
abstract void |
MultivariateMinimum.optimize(MultivariateFunction f,
double[] xvec,
double tolfx,
double tolx) |
The actual optimization routine
(needs to be implemented in a subclass of MultivariateMinimum).
|
void |
MultivariateMinimum.optimize(MultivariateFunction f,
double[] xvec,
double tolfx,
double tolx,
MinimiserMonitor monitor) |
The actual optimization routine
It finds a minimum close to vector x when the
absolute tolerance for each parameter is specified.
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void |
OrthogonalSearch.optimize(MultivariateFunction f,
double[] xvec,
double tolfx,
double tolx) |
|
void |
OrthogonalSearch.optimize(MultivariateFunction f,
double[] xvec,
double tolfx,
double tolx,
MinimiserMonitor monitor) |
|