Class BrentOptimizer
- java.lang.Object
-
- org.apache.commons.math3.optim.BaseOptimizer<UnivariatePointValuePair>
-
- org.apache.commons.math3.optim.univariate.UnivariateOptimizer
-
- org.apache.commons.math3.optim.univariate.BrentOptimizer
-
public class BrentOptimizer extends UnivariateOptimizer
For a function defined on some interval(lo, hi)
, this class finds an approximationx
to the point at which the function attains its minimum. It implements Richard Brent's algorithm (from his book "Algorithms for Minimization without Derivatives", p. 79) for finding minima of real univariate functions.
This code is an adaptation, partly based on the Python code from SciPy (module "optimize.py" v0.5); the original algorithm is also modified- to use an initial guess provided by the user,
- to ensure that the best point encountered is the one returned.
- Since:
- 2.0
-
-
Field Summary
Fields Modifier and Type Field Description private double
absoluteThreshold
Absolute threshold.private static double
GOLDEN_SECTION
Golden section.private static double
MIN_RELATIVE_TOLERANCE
Minimum relative tolerance.private double
relativeThreshold
Relative threshold.-
Fields inherited from class org.apache.commons.math3.optim.BaseOptimizer
evaluations, iterations
-
-
Constructor Summary
Constructors Constructor Description BrentOptimizer(double rel, double abs)
The arguments are used for implementing the original stopping criterion of Brent's algorithm.BrentOptimizer(double rel, double abs, ConvergenceChecker<UnivariatePointValuePair> checker)
The arguments are used implement the original stopping criterion of Brent's algorithm.
-
Method Summary
All Methods Instance Methods Concrete Methods Modifier and Type Method Description private UnivariatePointValuePair
best(UnivariatePointValuePair a, UnivariatePointValuePair b, boolean isMinim)
Selects the best of two points.protected UnivariatePointValuePair
doOptimize()
Performs the bulk of the optimization algorithm.-
Methods inherited from class org.apache.commons.math3.optim.univariate.UnivariateOptimizer
computeObjectiveValue, getGoalType, getMax, getMin, getStartValue, optimize, parseOptimizationData
-
Methods inherited from class org.apache.commons.math3.optim.BaseOptimizer
getConvergenceChecker, getEvaluations, getIterations, getMaxEvaluations, getMaxIterations, incrementEvaluationCount, incrementIterationCount, optimize
-
-
-
-
Field Detail
-
GOLDEN_SECTION
private static final double GOLDEN_SECTION
Golden section.
-
MIN_RELATIVE_TOLERANCE
private static final double MIN_RELATIVE_TOLERANCE
Minimum relative tolerance.
-
relativeThreshold
private final double relativeThreshold
Relative threshold.
-
absoluteThreshold
private final double absoluteThreshold
Absolute threshold.
-
-
Constructor Detail
-
BrentOptimizer
public BrentOptimizer(double rel, double abs, ConvergenceChecker<UnivariatePointValuePair> checker)
The arguments are used implement the original stopping criterion of Brent's algorithm.abs
andrel
define a tolerancetol = rel |x| + abs
.rel
should be no smaller than 2 macheps and preferably not much less than sqrt(macheps), where macheps is the relative machine precision.abs
must be positive.- Parameters:
rel
- Relative threshold.abs
- Absolute threshold.checker
- Additional, user-defined, convergence checking procedure.- Throws:
NotStrictlyPositiveException
- ifabs <= 0
.NumberIsTooSmallException
- ifrel < 2 * Math.ulp(1d)
.
-
BrentOptimizer
public BrentOptimizer(double rel, double abs)
The arguments are used for implementing the original stopping criterion of Brent's algorithm.abs
andrel
define a tolerancetol = rel |x| + abs
.rel
should be no smaller than 2 macheps and preferably not much less than sqrt(macheps), where macheps is the relative machine precision.abs
must be positive.- Parameters:
rel
- Relative threshold.abs
- Absolute threshold.- Throws:
NotStrictlyPositiveException
- ifabs <= 0
.NumberIsTooSmallException
- ifrel < 2 * Math.ulp(1d)
.
-
-
Method Detail
-
doOptimize
protected UnivariatePointValuePair doOptimize()
Performs the bulk of the optimization algorithm.- Specified by:
doOptimize
in classBaseOptimizer<UnivariatePointValuePair>
- Returns:
- the point/value pair giving the optimal value of the objective function.
-
best
private UnivariatePointValuePair best(UnivariatePointValuePair a, UnivariatePointValuePair b, boolean isMinim)
Selects the best of two points.- Parameters:
a
- Point and value.b
- Point and value.isMinim
-true
if the selected point must be the one with the lowest value.- Returns:
- the best point, or
null
ifa
andb
are bothnull
. Whena
andb
have the same function value,a
is returned.
-
-