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#Com.numericalmethod.suanshu.optimization.multivariate.unconstrained Classes and Interfaces - 44 results found.
NameDescriptionTypePackageFramework
AnnealingFunctionAn annealing function or a tempered proposal function gives the next proposal/state from the current state and temperature.Interfacecom.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.annealingfunctionSuanShu
BFGSMinimizerThe Broyden-Fletcher-Goldfarb-Shanno method is a quasi-Newton method to solve unconstrained nonlinear optimization problems.Classcom.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.quasinewtonSuanShu
BoltzAnnealingFunctionMatlab: @annealingboltz - The step has length square root of temperature, with direction uniformly at random.Classcom.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.annealingfunctionSuanShu
BoltzTemperatureFunction(T_k = T_0 / ln(k)).Classcom.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.temperaturefunctionSuanShu
BoxGSAAcceptanceProbabilityFunctionThis probability function boxes an unconstrained probability function so that when a proposed state is outside the box, it has a probability of 0.Classcom.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.acceptanceprobabilityfunctionSuanShu
BoxGSAAnnealingFunctionClasscom.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.annealingfunctionSuanShu
ConjugateGradientMinimizerA conjugate direction optimization method is performed by using sequential line search along directions that bear a strict mathematical relationship to one another.Classcom.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.conjugatedirectionSuanShu
DFPMinimizerThe Davidon-Fletcher-Powell method is a quasi-Newton method to solve unconstrained nonlinear optimization problems.Classcom.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.quasinewtonSuanShu
ExpTemperatureFunctionLogarithmic decay, where (T_k = T_0 * 0.Classcom.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.temperaturefunctionSuanShu
FastAnnealingFunctionMatlab default: @annealingfast - The step has length temperature, with direction uniformly at random.Classcom.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.annealingfunctionSuanShu
FastTemperatureFunctionLinear decay, where (T_k = T_0 / k).Classcom.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.temperaturefunctionSuanShu
FirstOrderMinimizerThis implements the steepest descent line search using the first order expansion of the Taylor's series.Classcom.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.steepestdescentSuanShu
FirstOrderMinimizer .Methodthe available methods to do line searchThe line search is done analytically.Classcom.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.steepestdescentSuanShu
FletcherLineSearchThis is Fletcher's inexact line search method.Classcom.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.linesearchSuanShu
FletcherReevesMinimizerThe Fletcher-Reeves method is a variant of the Conjugate-Gradient method.Classcom.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.conjugatedirectionSuanShu
GaussNewtonMinimizerThe Gauss-Newton method is a steepest descent method to minimize a real vector function in the form: f(x) = [f_1(x), f_2(x), .Classcom.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.steepestdescentSuanShu
GaussNewtonMinimizer .MySteepestDescentClasscom.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.steepestdescentSuanShu
GeneralizedSimulatedAnnealingMinimizerTsallis and Stariolo (1996) proposed this variant of SimulatedAnnealingMinimizer (SA).Classcom.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealingSuanShu
GSAAcceptanceProbabilityFunctionThe GSA acceptance probability function.Classcom.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.acceptanceprobabilityfunctionSuanShu
GSAAnnealingFunctionThe GSA proposal/annealing function.Classcom.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.annealingfunctionSuanShu
GSATemperatureFunctionThe GSA temperature function.Classcom.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.temperaturefunctionSuanShu
HuangMinimizerHuang's updating formula is a family of formulas which encompasses the rank-one, DFP, BFGS as well as some other formulas.Classcom.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.quasinewtonSuanShu
IterativeC2MaximizerA maximization problem is simply minimizing the negative of the objective function.Classcom.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2SuanShu
IterativeC2Maximizer .SolutionInterfacecom.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2SuanShu
IterativeC2MinimizerThis is a minimizer that minimizes a twice continuously differentiable, multivariate function.Interfacecom.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2SuanShu
IterativeMinimizerThis is an iterative multivariate minimizer.Interfacecom.numericalmethod.suanshu.optimization.multivariate.unconstrainedSuanShu
LineSearchA line search is often used in another minimization algorithm to improve the current solution in one iteration step.Interfacecom.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.linesearchSuanShu
LineSearch .SolutionThis is the solution to a line search minimization.Interfacecom.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.linesearchSuanShu
McCormickMinimizerThis is the McCormick method.Classcom.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.quasinewtonSuanShu
MetropolisAcceptanceProbabilityFunctionUses the classic Metropolis rule, f_{t+1}/f_t.Classcom.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.acceptanceprobabilityfunctionSuanShu
MultivariateMinimizerThis is a minimizer that minimizes a multivariate function or a Vector function.Interfacecom.numericalmethod.suanshu.optimization.multivariate.unconstrainedSuanShu
NelderMeadMinimizerThe Nelder-Mead method is a nonlinear optimization technique, which is well-defined for twice differentiable and unimodal problems.Classcom.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2SuanShu
NewtonRaphsonMinimizerThe Newton-Raphson method is a second order steepest descent method that is based on the quadratic approximation of the Taylor series.Classcom.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.steepestdescentSuanShu
PearsonMinimizerThis is the Pearson method.Classcom.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.quasinewtonSuanShu
PowellMinimizerPowell's algorithm, starting from an initial point, performs a series of line searches in one iteration.Classcom.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.conjugatedirectionSuanShu
QuasiNewtonMinimizerThe Quasi-Newton methods in optimization are for finding local maxima and minima of functions.Classcom.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.quasinewtonSuanShu
RankOneMinimizerThe Rank One method is a quasi-Newton method to solve unconstrained nonlinear optimization problems.Classcom.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.quasinewtonSuanShu
SimpleAnnealingFunctionThis annealing function takes a random step in a uniform direction, where the step size depends only on the temperature.Classcom.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.annealingfunctionSuanShu
SimpleTemperatureFunctionAbstract class for the common case where (T^V_t = T^A_t).Classcom.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.temperaturefunctionSuanShu
SimulatedAnnealingMinimizerSimulated Annealing is a global optimization meta-heuristic that is inspired by annealing in metallurgy.Classcom.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealingSuanShu
SteepestDescentMinimizerA steepest descent algorithm finds the minimum by moving along the negative of the steepest gradient direction.Classcom.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.steepestdescentSuanShu
TemperatureFunctionA temperature function defines a temperature schedule used in simulated annealing.Interfacecom.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.temperaturefunctionSuanShu
TemperedAcceptanceProbabilityFunctionA tempered acceptance probability function computes the probability that the next state transition will be accepted.Interfacecom.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.acceptanceprobabilityfunctionSuanShu
ZangwillMinimizerZangwill's algorithm is an improved version of Powell's algorithm.Classcom.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.conjugatedirectionSuanShu