Name | Description | Type | Package | Framework |
AnnealingFunction | An annealing function or a tempered proposal function gives the next proposal/state from the current state and temperature. | Interface | com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.annealingfunction | SuanShu |
|
BFGSMinimizer | The Broyden-Fletcher-Goldfarb-Shanno method is a quasi-Newton method to solve unconstrained nonlinear optimization problems. | Class | com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.quasinewton | SuanShu |
|
BoltzAnnealingFunction | Matlab: @annealingboltz - The step has length square root of temperature, with direction uniformly at random. | Class | com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.annealingfunction | SuanShu |
|
BoltzTemperatureFunction | (T_k = T_0 / ln(k)). | Class | com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.temperaturefunction | SuanShu |
|
BoxGSAAcceptanceProbabilityFunction | This probability function boxes an unconstrained probability function so that when a proposed state is outside the box, it has a probability of 0. | Class | com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.acceptanceprobabilityfunction | SuanShu |
|
BoxGSAAnnealingFunction | | Class | com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.annealingfunction | SuanShu |
|
ConjugateGradientMinimizer | A conjugate direction optimization method is performed by using sequential line search along directions that bear a strict mathematical relationship to one another. | Class | com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.conjugatedirection | SuanShu |
|
DFPMinimizer | The Davidon-Fletcher-Powell method is a quasi-Newton method to solve unconstrained nonlinear optimization problems. | Class | com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.quasinewton | SuanShu |
|
ExpTemperatureFunction | Logarithmic decay, where (T_k = T_0 * 0. | Class | com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.temperaturefunction | SuanShu |
|
FastAnnealingFunction | Matlab default: @annealingfast - The step has length temperature, with direction uniformly at random. | Class | com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.annealingfunction | SuanShu |
|
FastTemperatureFunction | Linear decay, where (T_k = T_0 / k). | Class | com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.temperaturefunction | SuanShu |
|
FirstOrderMinimizer | This implements the steepest descent line search using the first order expansion of the Taylor's series. | Class | com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.steepestdescent | SuanShu |
|
FirstOrderMinimizer .Method | the available methods to do line searchThe line search is done analytically. | Class | com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.steepestdescent | SuanShu |
|
FletcherLineSearch | This is Fletcher's inexact line search method. | Class | com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.linesearch | SuanShu |
|
FletcherReevesMinimizer | The Fletcher-Reeves method is a variant of the Conjugate-Gradient method. | Class | com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.conjugatedirection | SuanShu |
|
GaussNewtonMinimizer | The 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), . | Class | com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.steepestdescent | SuanShu |
|
GaussNewtonMinimizer .MySteepestDescent | | Class | com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.steepestdescent | SuanShu |
|
GeneralizedSimulatedAnnealingMinimizer | Tsallis and Stariolo (1996) proposed this variant of SimulatedAnnealingMinimizer (SA). | Class | com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing | SuanShu |
|
GSAAcceptanceProbabilityFunction | The GSA acceptance probability function. | Class | com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.acceptanceprobabilityfunction | SuanShu |
|
GSAAnnealingFunction | The GSA proposal/annealing function. | Class | com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.annealingfunction | SuanShu |
|
GSATemperatureFunction | The GSA temperature function. | Class | com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.temperaturefunction | SuanShu |
|
HuangMinimizer | Huang's updating formula is a family of formulas which encompasses the rank-one, DFP, BFGS as well as some other formulas. | Class | com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.quasinewton | SuanShu |
|
IterativeC2Maximizer | A maximization problem is simply minimizing the negative of the objective function. | Class | com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2 | SuanShu |
|
IterativeC2Maximizer .Solution | | Interface | com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2 | SuanShu |
|
IterativeC2Minimizer | This is a minimizer that minimizes a twice continuously differentiable, multivariate function. | Interface | com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2 | SuanShu |
|
IterativeMinimizer | This is an iterative multivariate minimizer. | Interface | com.numericalmethod.suanshu.optimization.multivariate.unconstrained | SuanShu |
|
LineSearch | A line search is often used in another minimization algorithm to improve the current solution in one iteration step. | Interface | com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.linesearch | SuanShu |
|
LineSearch .Solution | This is the solution to a line search minimization. | Interface | com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.linesearch | SuanShu |
|
McCormickMinimizer | This is the McCormick method. | Class | com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.quasinewton | SuanShu |
|
MetropolisAcceptanceProbabilityFunction | Uses the classic Metropolis rule, f_{t+1}/f_t. | Class | com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.acceptanceprobabilityfunction | SuanShu |
|
MultivariateMinimizer | This is a minimizer that minimizes a multivariate function or a Vector function. | Interface | com.numericalmethod.suanshu.optimization.multivariate.unconstrained | SuanShu |
|
NelderMeadMinimizer | The Nelder-Mead method is a nonlinear optimization technique, which is well-defined for twice differentiable and unimodal problems. | Class | com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2 | SuanShu |
|
NewtonRaphsonMinimizer | The Newton-Raphson method is a second order steepest descent method that is based on the quadratic approximation of the Taylor series. | Class | com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.steepestdescent | SuanShu |
|
PearsonMinimizer | This is the Pearson method. | Class | com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.quasinewton | SuanShu |
|
PowellMinimizer | Powell's algorithm, starting from an initial point, performs a series of line searches in one iteration. | Class | com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.conjugatedirection | SuanShu |
|
QuasiNewtonMinimizer | The Quasi-Newton methods in optimization are for finding local maxima and minima of functions. | Class | com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.quasinewton | SuanShu |
|
RankOneMinimizer | The Rank One method is a quasi-Newton method to solve unconstrained nonlinear optimization problems. | Class | com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.quasinewton | SuanShu |
|
SimpleAnnealingFunction | This annealing function takes a random step in a uniform direction, where the step size depends only on the temperature. | Class | com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.annealingfunction | SuanShu |
|
SimpleTemperatureFunction | Abstract class for the common case where (T^V_t = T^A_t). | Class | com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.temperaturefunction | SuanShu |
|
SimulatedAnnealingMinimizer | Simulated Annealing is a global optimization meta-heuristic that is inspired by annealing in metallurgy. | Class | com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing | SuanShu |
|
SteepestDescentMinimizer | A steepest descent algorithm finds the minimum by moving along the negative of the steepest gradient direction. | Class | com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.steepestdescent | SuanShu |
|
TemperatureFunction | A temperature function defines a temperature schedule used in simulated annealing. | Interface | com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.temperaturefunction | SuanShu |
|
TemperedAcceptanceProbabilityFunction | A tempered acceptance probability function computes the probability that the next state transition will be accepted. | Interface | com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.acceptanceprobabilityfunction | SuanShu |
|
ZangwillMinimizer | Zangwill's algorithm is an improved version of Powell's algorithm. | Class | com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.conjugatedirection | SuanShu |