Uses of Class pal.math.MultivariateMinimum

Packages that use MultivariateMinimum
Package	Description
pal.eval	Classes for evaluating evolutionary hypothesis (chi-square and likelihood criteria) and estimating model parameters.
pal.math	Classes for math stuff such as optimisation, numerical derivatives, matrix exponentials, random numbers, special function etc.
pal.treesearch

Uses of MultivariateMinimum in pal.eval

Methods in pal.eval with parameters of type MultivariateMinimum
Modifier and Type	Method	Description
`static double`	LikelihoodOptimiser.`optimiseAlternate(ParameterizedTree tree, Alignment alignment, SubstitutionModel model, MultivariateMinimum minimiser, int fxFracDigits, int xFracDigits)`	Optimise parameters to acheive maximum likelihood using an alternating stategy.
`static double`	LikelihoodOptimiser.`optimiseAlternate(ParameterizedTree tree, Alignment alignment, SubstitutionModel model, MultivariateMinimum minimiser, int fxFracDigits, int xFracDigits, MinimiserMonitor monitor)`	Optimise parameters to acheive maximum likelihood using an alternating stategy.
`static double`	LikelihoodOptimiser.`optimiseCombined(ParameterizedTree tree, Alignment alignment, SubstitutionModel model, MultivariateMinimum minimiser, int fxFracDigits, int xFracDigits)`	Optimise parameters to acheive maximum likelihood using a combined stategy.
`static double`	LikelihoodOptimiser.`optimiseCombined(ParameterizedTree tree, Alignment alignment, SubstitutionModel model, MultivariateMinimum minimiser, int fxFracDigits, int xFracDigits, MinimiserMonitor monitor)`	Optimise parameters to acheive maximum likelihood using a combined stategy.
`double`	LikelihoodOptimiser.`optimiseLogLikelihood(Parameterized parameters, MultivariateMinimum minimiser, int fxFracDigits, int xFracDigits)`
`double`	LikelihoodOptimiser.`optimiseLogLikelihood(Parameterized parameters, MultivariateMinimum minimiser, int fxFracDigits, int xFracDigits, MinimiserMonitor monitor)`
`static double`	LikelihoodOptimiser.`optimiseModel(Tree tree, Alignment alignment, SubstitutionModel model, MultivariateMinimum minimiser, int fxFracDigits, int xFracDigits, MinimiserMonitor monitor)`	Optimise model parameters only to acheive maximum likelihood using a combined stategy.
`double`	ChiSquareValue.`optimiseParameters(MultivariateMinimum mm)`	optimise parameters of a tree by minimising its chi-square value (tree must be a ParameterizedTree)
`double`	LikelihoodValue.`optimiseParameters(MultivariateMinimum mm)`	optimise parameters of tree by maximising its likelihood (this assumes that tree is a ParameterizedTree)
`static double`	LikelihoodOptimiser.`optimiseTree(ParameterizedTree tree, Alignment alignment, SubstitutionModel model, MultivariateMinimum minimiser, int fxFracDigits, int xFracDigits)`	Optimise tree branchlengths only to acheive maximum likelihood using a combined stategy.
`static double`	LikelihoodOptimiser.`optimiseTree(ParameterizedTree tree, Alignment alignment, SubstitutionModel model, MultivariateMinimum minimiser, int fxFracDigits, int xFracDigits, MinimiserMonitor monitor)`	Optimise tree branchlengths only to acheive maximum likelihood using a combined stategy.
`double`	DemographicValue.`optimize(MultivariateMinimum givenMvm)`	optimize log-likelihood value and compute corresponding SEs given an optimizer

Uses of MultivariateMinimum in pal.math

Subclasses of MultivariateMinimum in pal.math
Modifier and Type	Class	Description
`class`	`ConjugateDirectionSearch`	methods for minimization of a real-valued function of several variables without using derivatives (Brent's modification of a conjugate direction search method proposed by Powell)
`class`	`ConjugateGradientSearch`	minimization of a real-valued function of several variables using a the nonlinear conjugate gradient method where several variants of the direction update are available (Fletcher-Reeves, Polak-Ribiere, Beale-Sorenson, Hestenes-Stiefel) and bounds are respected.
`class`	`DifferentialEvolution`	global minimization of a real-valued function of several variables without using derivatives using a genetic algorithm (Differential Evolution)
`class`	`GeneralizedDEOptimizer`	Provides an general interface to the DifferentialEvolution class that is not tied to a certain number of parameters (as DifferentialEvolution is).
`class`	`OrthogonalSearch`	minimization of a real-valued function of several variables without using derivatives, using the simple strategy of optimizing variables one by one.

Methods in pal.math that return MultivariateMinimum
Modifier and Type	Method	Description
`MultivariateMinimum`	MultivariateMinimum.Factory.`generateNewMinimiser()`	Generate a new Multivariate Minimum

Uses of MultivariateMinimum in pal.treesearch

Methods in pal.treesearch with parameters of type MultivariateMinimum
Modifier and Type	Method	Description
`UndoableAction`	UnrootedMLSearcher.`getBranchLengthWithModelOptimiseAction(StoppingCriteria.Factory stopper, MultivariateMinimum minimiser, int fxFracDigits, int xFracDigits)`
`UndoableAction`	UnrootedMLSearcher.`getModelOptimiseAction(MultivariateMinimum minimiser, int fxFracDigits, int xFracDigits)`
`UndoableAction`	UnrootedMLSearcher.`getModelOptimiseAction(MultivariateMinimum minimiser, MinimiserMonitor monitor, int fxFracDigits, int xFracDigits)`
`double`	GeneralLikelihoodSearcher.`optimiseAllFullHeirarchy(StoppingCriteria mainStopper, StoppingCriteria subStopper, MultivariateMinimum rateMinimiser, int fxFracDigits, int xFracDigits, AlgorithmCallback callback, SearchMonitor monitor, MinimiserMonitor rateMonitor)`
`double`	GeneralConstraintGroupManager.`optimiseAllGlobalClockConstraints(MultivariateMinimum minimiser, GeneralConstraintGroupManager.LikelihoodScoreAccess scoreAccess, int fxFracDigits, int xFracDigits, MinimiserMonitor rateMonitor)`	Optimise all the global clock parameters related to this group
`double`	GeneralLikelihoodSearcher.`optimiseAllPlusSubstitutionModel(StoppingCriteria stopper, MultivariateMinimum rateMinimiser, MultivariateMinimum substitutionModelMinimiser, int fxFracDigits, int xFracDigits, AlgorithmCallback callback, SearchMonitor monitor, int substitutionModelOptimiseFrequency, MinimiserMonitor substitutionModelMonitor, MinimiserMonitor rateMonitor)`
`double`	GeneralLikelihoodSearcher.`optimiseAllSimple(StoppingCriteria stopper, MultivariateMinimum rateMinimiser, int fxFracDigits, int xFracDigits, AlgorithmCallback callback)`
`double`	GeneralLikelihoodSearcher.`optimiseAllSimple(StoppingCriteria stopper, MultivariateMinimum rateMinimiser, int fxFracDigits, int xFracDigits, AlgorithmCallback callback, SearchMonitor monitor, MinimiserMonitor rateMonitor)`
`double`	GeneralLikelihoodSearcher.`optimiseAllSimple(StoppingCriteria stopper, MultivariateMinimum rateMinimiser, int fxFracDigits, int xFracDigits, AlgorithmCallback callback, SearchMonitor monitor, MinimiserMonitor rateMonitor, int groupOptimistionType)`
`double`	GeneralLikelihoodSearcher.`optimiseAllSimpleHeirarchy(StoppingCriteria stopper, MultivariateMinimum rateMinimiser, int fxFracDigits, int xFracDigits, AlgorithmCallback callback, SearchMonitor monitor, MinimiserMonitor rateMonitor)`
`double`	GeneralLikelihoodSearcher.`optimiseConstraintRateModels(MultivariateMinimum minimiser, int fxFracDigits, int xFracDigits, MinimiserMonitor rateMonitor)`
`double`	GeneralConstraintGroupManager.`optimisePrimaryGlobalClockConstraints(MultivariateMinimum minimiser, GeneralConstraintGroupManager.LikelihoodScoreAccess scoreAccess, int fxFracDigits, int xFracDigits, MinimiserMonitor rateMonitor)`	Optimise the global clock parameters marked as primary related to this group
`double`	GeneralConstraintGroupManager.`optimiseSecondaryGlobalClockConstraints(MultivariateMinimum minimiser, GeneralConstraintGroupManager.LikelihoodScoreAccess scoreAccess, int fxFracDigits, int xFracDigits, MinimiserMonitor rateMonitor)`	Optimise the global clock parameters marked as secondary related to this group
`double`	GeneralLikelihoodSearcher.`optimiseSubstitutionModels(MultivariateMinimum minimiser, int fxFracDigits, int xFracDigits, MinimiserMonitor monitor)`