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#Com.numericalmethod.suanshu.optimization Classes and Interfaces - 225 results found.
NameDescriptionTypePackageFramework
AbsoluteErrorPenaltyThis penalty function sums up the absolute error penalties.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.general.penaltymethodSuanShu
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
AntoniouLu2007This implementation is based on Algorithm 14.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.interiorpointSuanShu
Best1BinThe Best-1-Bin rule is the same as the Rand-1-Bin rule, except that it always pick the best candidate in the population to be the base.Classcom.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.deoptimSuanShu
Best2BinThe Best-1-Bin rule always picks the best chromosome as the base.Classcom.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.deoptimSuanShu
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
BoxConstraintsThis represents the lower and upper bounds for a variable.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.constraint.linearSuanShu
BoxConstraints .BoundA bound constraint for a variable.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.constraint.linearSuanShu
BoxGeneralizedSimulatedAnnealingMinimizerThis is an extension to GeneralizedSimulatedAnnealingMinimizer, which allows adding box constraints to bound solutions.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.general.boxSuanShu
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
BoxMinimizerA box minimizer solves a BoxOptimProblem.Interfacecom.numericalmethod.suanshu.optimization.multivariate.constrainedSuanShu
BoxOptimProblemA box constrained optimization problem, for which a solution must be within fixed bounds.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.problemSuanShu
BracketSearchMinimizerThis class provides implementation support for those univariate optimization algorithms that are based on bracketing.Classcom.numericalmethod.suanshu.optimization.univariate.bracketsearchSuanShu
BrentMinimizerBrent's algorithm is the preferred method for finding the minimum of a univariate function.Classcom.numericalmethod.suanshu.optimization.univariate.bracketsearchSuanShu
BruteForceIPMinimizerThis implementation solves an integral constrained minimization problem by brute force search for all possible integer combinations.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.integer.bruteforceSuanShu
BruteForceIPProblemThis implementation is an integral constrained minimization problem that has enumerable integral domains.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.integer.bruteforceSuanShu
BruteForceIPProblem .IntegerDomainThis specifies the integral domain for an integral variable, i.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.integer.bruteforceSuanShu
C2OptimProblemThis is an optimization problem of a real valued function that is twice differentiable.Interfacecom.numericalmethod.suanshu.optimization.problemSuanShu
C2OptimProblemImplThis is an optimization problem of a real valued function: (max_x f(x)).Classcom.numericalmethod.suanshu.optimization.problemSuanShu
CentralPathA central path is a solution to both the primal and dual problems of a semi-definite programming problem.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.pathfollowingSuanShu
ChromosomeA chromosome is a representation of a solution to an optimization problem.Interfacecom.numericalmethod.suanshu.optimization.multivariate.geneticalgorithmSuanShu
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
ConstrainedCellFactoryThis defines a Differential Evolution operator that takes in account constraints.Classcom.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.deoptim.constrainedSuanShu
ConstrainedMinimizerA constrained minimizer solves a constrained optimization problem, namely, ConstrainedOptimProblem.Interfacecom.numericalmethod.suanshu.optimization.multivariate.constrainedSuanShu
ConstrainedOptimProblemA constrained optimization problem takes this form.Interfacecom.numericalmethod.suanshu.optimization.multivariate.constrained.problemSuanShu
ConstrainedOptimProblemImpl1This implements a constrained optimization problem for a function f subject to equality and less-than-or-equal-to constraints.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.problemSuanShu
ConstrainedOptimSubProblemA constrained optimization sub-problem takes this form.Interfacecom.numericalmethod.suanshu.optimization.multivariate.constrainedSuanShu
ConstraintsA set of constraints for a (real-valued) optimization problem is a set of functions.Interfacecom.numericalmethod.suanshu.optimization.multivariate.constrained.constraintSuanShu
ConstraintsUtilsThese are the utility functions for manipulating Constraints.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.constraintSuanShu
CourantPenaltyThis penalty function sums up the squared error penalties.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.general.penaltymethodSuanShu
CSDPMinimizerSee Also:"Borchers, Brian, "CSDP, a C Library for Semidefinite Programming", Optimization Methods and Software 11(1): 613-623, 1999.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.pathfollowingSuanShu
DefaultSimplexA simplex optimization algorithm, e.Classcom.numericalmethod.suanshu.optimization.multivariate.initializationSuanShu
DEOptimDifferential Evolution (DE) is a global optimization method.Classcom.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.deoptimSuanShu
DEOptim .NewCellFactoryThis factory constructs a new DEOptimCellFactory for each minimization problem.Interfacecom.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.deoptimSuanShu
DEOptimCellFactoryA DEOptimCellFactory produces DEOptimCellFactory.Classcom.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.deoptimSuanShu
DFPMinimizerThe Davidon-Fletcher-Powell method is a quasi-Newton method to solve unconstrained nonlinear optimization problems.Classcom.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.quasinewtonSuanShu
EqualityConstraintsThe domain of an optimization problem may be restricted by equality constraints.Interfacecom.numericalmethod.suanshu.optimization.multivariate.constrained.constraintSuanShu
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
FerrisMangasarianWrightPhase1The phase 1 procedure finds a feasible table from an infeasible one by pivoting the simplex table of a related problem.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplexSuanShu
FerrisMangasarianWrightPhase2This implementation solves a canonical linear programming problem that does not need preprocessing its simplex table.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solverSuanShu
FerrisMangasarianWrightScheme2The scheme 2 procedure removes equalities and free variables.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplexSuanShu
FibonaccMinimizerThe Fibonacci search is a dichotomous search where a bracketing interval is sub-divided by the Fibonacci ratio.Classcom.numericalmethod.suanshu.optimization.univariate.bracketsearchSuanShu
FirstGenerationThis interface allows customization of how the first pool of chromosomes is generated.Interfacecom.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.simplegrid.firstgenerationSuanShu
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
FletcherPenaltyThis penalty function sums up the squared costs penalties.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.general.penaltymethodSuanShu
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
GeneralConstraintsThe real-valued constraints define the domain (feasible regions) for a real-valued objective function in a constrained optimization problem.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.constraint.generalSuanShu
GeneralEqualityConstraintsThis is the collection of equality constraints for an optimization problem.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.constraint.generalSuanShu
GeneralGreaterThanConstraintsThis is the collection of greater-than-or-equal-to constraints for an optimization problem.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.constraint.generalSuanShu
GeneralizedSimulatedAnnealingMinimizerTsallis and Stariolo (1996) proposed this variant of SimulatedAnnealingMinimizer (SA).Classcom.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealingSuanShu
GeneralLessThanConstraintsThis is the collection of less-than or equal-to constraints for an optimization problem.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.constraint.generalSuanShu
GeneticAlgorithmA genetic algorithm (GA) is a search heuristic that mimics the process of natural evolution.Classcom.numericalmethod.suanshu.optimization.multivariate.geneticalgorithmSuanShu
GlobalSearchByLocalMinimizerThis minimizer is a global optimization method.Classcom.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.localSuanShu
GoldenMinimizerThis is the golden section univariate minimization algorithm.Classcom.numericalmethod.suanshu.optimization.univariate.bracketsearchSuanShu
GomoryMixedCutMinimizerThis cutting-plane implementation uses Gomory's mixed cut method.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.integer.linear.cuttingplaneSuanShu
GomoryMixedCutMinimizer .MyCutterThis is Gomory's mixed cut.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.integer.linear.cuttingplaneSuanShu
GomoryPureCutMinimizerThis cutting-plane implementation uses Gomory's pure cut method for pure integer programming, in which all variables are integral.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.integer.linear.cuttingplaneSuanShu
GomoryPureCutMinimizer .MyCutterThis is Gomory's pure cut.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.integer.linear.cuttingplaneSuanShu
GreaterThanConstraintsThe domain of an optimization problem may be restricted by greater-than or equal-to constraints.Interfacecom.numericalmethod.suanshu.optimization.multivariate.constrained.constraintSuanShu
GridSearchMinimizerThis performs a grid search to find the minimum of a univariate function.Classcom.numericalmethod.suanshu.optimization.univariateSuanShu
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
HomogeneousPathFollowingMinimizerThis implementation solves a Semi-Definite Programming problem using the Homogeneous Self-Dual Path-Following algorithm.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.pathfollowingSuanShu
HpThis is the symmetrization operator as defined in equation (6) in the reference.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.pathfollowingSuanShu
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
ILPBranchAndBoundMinimizerThis is a Branch-and-Bound algorithm that solves Integer Linear Programming problems.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.integer.linear.bbSuanShu
ILPBranchAndBoundMinimizer .ActiveListFactoryThis factory constructs a new instance of ActiveList for each Integer Linear Programming problem.Interfacecom.numericalmethod.suanshu.optimization.multivariate.constrained.integer.linear.bbSuanShu
ILPNodeThis is the branch-and-bound node used in conjunction with ILPBranchAndBoundMinimizer to solve an Integer Linear Programming problem.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.integer.linear.bbSuanShu
ILPProblemA linear program in real variables is said to be integral if it has at least one optimal solution which is integral.Interfacecom.numericalmethod.suanshu.optimization.multivariate.constrained.integer.linear.problemSuanShu
ILPProblemImpl1This implementation is an ILP problem, in which the variables can be real or integral.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.integer.linear.problemSuanShu
InitialsFactorySome optimization algorithms, e.Interfacecom.numericalmethod.suanshu.optimization.multivariate.initializationSuanShu
IntegralConstrainedCellFactoryThis implementation defines the constrained Differential Evolution operators that solve an Integer Programming problem.Classcom.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.deoptim.constrainedSuanShu
IntegralConstrainedCellFactory .AllIntegersThis integral constraint makes all variables in the objective function integral variables.Classcom.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.deoptim.constrainedSuanShu
IntegralConstrainedCellFactory .IntegerConstraintThe integral constraints are defined by implementing this interface.Interfacecom.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.deoptim.constrainedSuanShu
IntegralConstrainedCellFactory .SomeIntegersThis integral constraint makes some variables in the objective function integral variables.Classcom.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.deoptim.constrainedSuanShu
IPMinimizerAn Integer Programming minimizer minimizes an objective function subject to equality/inequality constraints as well as integral constraints.Interfacecom.numericalmethod.suanshu.optimization.multivariate.constrained.integerSuanShu
IPProblemAn Integer Programming problem is a mathematical optimization or feasibility program in which some or all of the variables are restricted to be integers.Interfacecom.numericalmethod.suanshu.optimization.multivariate.constrained.integerSuanShu
IPProblemImpl1This is an implementation of a general Integer Programming problem in which some variables take only integers.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.integerSuanShu
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
IterativeSolutionMany minimization algorithms work by starting from some given initials and iteratively moving toward an approximate solution.Interfacecom.numericalmethod.suanshu.optimizationSuanShu
JordanExchangeJordan Exchange swaps the r-th entering variable (row) with the s-th leaving variable (column) in a matrix A.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplexSuanShu
LeastPthThe least p-th minmax algorithm minimizes the maximal error/loss (function): min_x max_{omega in S} e(x, omega)Classcom.numericalmethod.suanshu.optimization.multivariate.minmaxSuanShu
LessThanConstraintsThe domain of an optimization problem may be restricted by less-than or equal-to constraints.Interfacecom.numericalmethod.suanshu.optimization.multivariate.constrained.constraintSuanShu
LinearConstraintsThis is a collection of linear constraints for a real-valued optimization problem.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.constraint.linearSuanShu
LinearEqualityConstraintsThis is a collection of linear equality constraints.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.constraint.linearSuanShu
LinearGreaterThanConstraintsThis is a collection of linear greater-than-or-equal-to constraints.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.constraint.linearSuanShu
LinearLessThanConstraintsThis is a collection of linear less-than-or-equal-to constraints.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.constraint.linearSuanShu
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
LocalSearchCellFactoryA LocalSearchCellFactory produces LocalSearchCellFactory.Classcom.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.localSuanShu
LocalSearchCellFactory .MinimizerFactoryThis factory constructs a new Minimizer for each mutation operation.Interfacecom.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.localSuanShu
LowerBoundConstraintsClasscom.numericalmethod.suanshu.optimization.multivariate.constrained.constraint.linearSuanShu
LPBoundedMinimizerThis is the solution to a bounded linear programming problem.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solutionSuanShu
LPCanonicalProblem1Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.problemSuanShu
LPCanonicalProblem2Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.problemSuanShu
LPCanonicalSolverThis is an LP solver that solves a canonical LP problem in the following form.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solverSuanShu
LPDimensionNotMatchedThis is the exception thrown when the dimensions of the objective function and constraints of a linear programming problem are inconsistent.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.exceptionSuanShu
LPEmptyCostVectorThis is the exception thrown when there is no objective function in a linear programming problem.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.exceptionSuanShu
LPExceptionThis is the exception thrown when there is any problem when solving a linear programming problem.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.exceptionSuanShu
LPInfeasibleThis is the exception thrown when the LP problem is infeasible, i.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.exceptionSuanShu
LPMinimizerAn LP minimizer minimizes the objective of an LP problem, satisfying all the constraints.Interfacecom.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lpSuanShu
LPNoConstraintThis is the exception thrown when there is no linear constraint found for the LP problem.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.exceptionSuanShu
LPProblemA linear programming (LP) problem minimizes a linear objective function subject to a collection of linear constraints.Interfacecom.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.problemSuanShu
LPProblemImpl1This is an implementation of a linear programming problem, LPProblem.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.problemSuanShu
LPRuntimeExceptionThis is the exception thrown when there is any problem when constructing a linear programming problem.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.exceptionSuanShu
LPSimplexMinimizerA simplex LP minimizer can be read off from the solution simplex table.Interfacecom.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solutionSuanShu
LPSimplexSolutionThe solution to a linear programming problem using a simplex method contains an LPSimplexMinimizer.Interfacecom.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solutionSuanShu
LPSimplexSolverA simplex solver works toward an LP solution by sequentially applying Jordan exchange to a simplex table.Interfacecom.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solverSuanShu
LPSolutionA solution to an LP problem contains all information about solving an LP problem such as whether the problem has a solution (bounded), how many minimizers it has, and the minimum.Interfacecom.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lpSuanShu
LPSolverAn LP solver solves a Linear Programming (LP) problem.Interfacecom.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lpSuanShu
LPStandardProblemClasscom.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.problemSuanShu
LPTwoPhaseSolverThis implementation solves a linear programming problem, LPProblem, using a two-step approach.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solverSuanShu
LPUnboundedThis is the exception thrown when the LP problem is unbounded.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.exceptionSuanShu
LPUnboundedMinimizerThis is the solution to an unbounded linear programming problem.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solutionSuanShu
LPUnboundedMinimizerScheme2This is the solution to an unbounded linear programming problem found in scheme 2.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solutionSuanShu
MaximizationSolutionThis is the solution to a maximization problem.Interfacecom.numericalmethod.suanshu.optimizationSuanShu
MaxmizerThis interface represents an optimization algorithm that maximizers a real valued objective function, one or multi dimension.Interfacecom.numericalmethod.suanshu.optimizationSuanShu
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
MinimizationSolutionThis is the solution to a minimization problem.Interfacecom.numericalmethod.suanshu.optimizationSuanShu
MinimizerThis interface represents an optimization algorithm that minimizes a real valued objective function, one or multi dimension.Interfacecom.numericalmethod.suanshu.optimizationSuanShu
MinMaxMinimizerA minmax minimizer minimizes a minmax problem.Interfacecom.numericalmethod.suanshu.optimization.multivariate.minmaxSuanShu
MultiplierPenaltyA multiplier penalty function allows different weights to be assigned to the constraints.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.general.penaltymethodSuanShu
MultivariateMinimizerThis is a minimizer that minimizes a multivariate function or a Vector function.Interfacecom.numericalmethod.suanshu.optimization.multivariate.unconstrainedSuanShu
NaiveRuleThis pivoting rule chooses the column with the most negative reduced cost.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.pivotingSuanShu
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
NonNegativityConstraintOptimProblemThis is a constrained optimization problem for a function which has all non-negative variables.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.problemSuanShu
NonNegativityConstraintsThese constraints ensures that for all variables are non-negative.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.constraint.linearSuanShu
OptimizerOptimization, or mathematical programming, refers to choosing the best element from some set of available alternatives.Interfacecom.numericalmethod.suanshu.optimizationSuanShu
OptimProblemThis is an optimization problem that minimizes a real valued objective function, one or multi dimension.Interfacecom.numericalmethod.suanshu.optimization.problemSuanShu
PearsonMinimizerThis is the Pearson method.Classcom.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.quasinewtonSuanShu
PenaltyFunctionA function P: Rn -> R is a penalty function for a constrained optimization problem if it has these properties.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.general.penaltymethodSuanShu
PenaltyMethodMinimizerThe penalty method is an algorithm for solving a constrained minimization problem with general It replaces a constrained optimization problem by a series of unconstrained problemsClasscom.numericalmethod.suanshu.optimization.multivariate.constrained.general.penaltymethodSuanShu
PenaltyMethodMinimizer .PenaltyFunctionFactoryFor each constrained optimization problem, the solver creates a new penalty function for it.Interfacecom.numericalmethod.suanshu.optimization.multivariate.constrained.general.penaltymethodSuanShu
PerturbationAroundPointThe initial population is generated by adding a variance around a given initial.Classcom.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.simplegrid.firstgenerationSuanShu
PortfolioRiskExactSigma .DefaultRootComputes the matrix root by Cholesky and on failure by MatrixRootByDiagonalization.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimizationSuanShu
PortfolioRiskExactSigma .DiagonalizationComputes the matrix root by MatrixRootByDiagonalization.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimizationSuanShu
PortfolioRiskExactSigma .MatrixRootSpecifies the method to compute the root of a matrix.Interfacecom.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimizationSuanShu
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
PrimalDualInteriorPointMinimizerSolves a Dual Second Order Conic Programming problem using the Primal Dual Interior Point 2014/1/9: This solver is tested up to 6000 variables and 26000 constraints.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.interiorpointSuanShu
PrimalDualPathFollowingMinimizerThe Primal-Dual Path-Following algorithm is an interior point method that solves Semi-Definite Programming problems.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.pathfollowingSuanShu
PrimalDualSolutionThe vector set {x, s, y} is a solution to both the primal and dual SOCP problems.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.interiorpointSuanShu
PureILPProblemThis is a pure integer linear programming problem, in which all variables are integral.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.integer.linear.problemSuanShu
QPDualActiveSetMinimizerThis implementation solves a Quadratic Programming problem using the dual active set algorithm.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.activesetSuanShu
QPExceptionThis is the exception thrown when there is an error solving a quadratic programming problem.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qpSuanShu
QPInfeasibleThis is the exception thrown by a quadratic programming solver when the quadratic programming problem is infeasible, i.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qpSuanShu
QPPrimalActiveSetMinimizerThis implementation solves a Quadratic Programming problem using the Primal Active Set algorithm.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.activesetSuanShu
QPProblemQuadratic Programming is the problem of optimizing (minimizing) a quadratic function of several variables subject to linear constraints on these variables.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.problemSuanShu
QPProblemOnlyEqualityConstraintsA quadratic programming problem with only equality constraints can be converted into a equivalent quadratic programming problem without constraints, hence a mere quadratic function.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.problemSuanShu
QPSimpleMinimizerThese are the utility functions to solve simple quadratic programming problems that admit analytical solutions.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qpSuanShu
QPSolutionThis is a solution to a quadratic programming problem.Interfacecom.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qpSuanShu
QuasiNewtonMinimizerThe Quasi-Newton methods in optimization are for finding local maxima and minima of functions.Classcom.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.quasinewtonSuanShu
Rand1BinThe Rand-1-Bin rule is defined by: mutation by adding a scaled, randomly sampled vector difference to a third vectorClasscom.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.deoptimSuanShu
RankOneMinimizerThe Rank One method is a quasi-Newton method to solve unconstrained nonlinear optimization problems.Classcom.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.quasinewtonSuanShu
RealScalarFunctionChromosomeThis chromosome encodes a real valued function.Classcom.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.simplegridSuanShu
SDPDualProblemA dual SDP problem, as in equation 14.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.problemSuanShu
SDPDualProblem .EqualityConstraintsThis is the collection of equality constraints: sum_{i=1}^{p}y_imathbf{A_i}+ extbf{S} = extbf{C}, extbf{S} succeq extbf{0}Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.problemSuanShu
SDPPrimalProblemA Primal SDP problem, as in equation 14.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.problemSuanShu
SDPT3v4This implements Algorithm_IPC, the SOCP interior point algorithm in SDPT3 version 4.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.interiorpointSuanShu
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
SimpleCellFactoryA SimpleCellFactory produces SimpleCellFactory.Classcom.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.simplegridSuanShu
SimpleGridMinimizerThis minimizer is a simple global optimization method.Classcom.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.simplegridSuanShu
SimpleGridMinimizer .NewCellFactoryCtorThis factory constructs a new SimpleCellFactory for each minimization problem.Interfacecom.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.simplegridSuanShu
SimpleTemperatureFunctionAbstract class for the common case where (T^V_t = T^A_t).Classcom.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.temperaturefunctionSuanShu
SimplexCuttingPlaneMinimizerThe use of cutting planes to solve Mixed Integer Linear Programming (MILP) problems was introduced by Ralph E Gomory.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.integer.linear.cuttingplaneSuanShu
SimplexCuttingPlaneMinimizer .CutterFactoryThis factory constructs a new Cutter for each MILP problem.Interfacecom.numericalmethod.suanshu.optimization.multivariate.constrained.integer.linear.cuttingplaneSuanShu
SimplexCuttingPlaneMinimizer .CutterFactory .CutterA Cutter defines how to cut a simplex table, i.Interfacecom.numericalmethod.suanshu.optimization.multivariate.constrained.integer.linear.cuttingplaneSuanShu
SimplexPivotingA simplex pivoting finds a row and column to exchange to reduce the cost function.Interfacecom.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.pivotingSuanShu
SimplexPivoting .PivotClasscom.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.pivotingSuanShu
SimplexTableThis is a simplex table used to solve a linear programming problem using a simplex method.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplexSuanShu
SimplexTable .LabelClasscom.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplexSuanShu
SimplexTable .LabelTypethe artificial variable, x0, pp.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplexSuanShu
SimulatedAnnealingMinimizerSimulated Annealing is a global optimization meta-heuristic that is inspired by annealing in metallurgy.Classcom.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealingSuanShu
SmallestSubscriptRuleBland's smallest-subscript rule is for anti-cycling in choosing a pivot.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.pivotingSuanShu
SOCPDualProblemThis is the Dual Second Order Conic Programming problem.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problemSuanShu
SOCPDualProblem .EqualityConstraintsClasscom.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problemSuanShu
SOCPGeneralConstraintThis represents the SOCP general constraint of this form.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problemSuanShu
SOCPGeneralConstraintsThis represents a set of SOCP general constraints of this form.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problemSuanShu
SOCPGeneralProblemMany convex programming problems can be represented in the following form.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problemSuanShu
SOCPPortfolioConstraintAn SOCP constraint for portfolio optimization, e.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimizationSuanShu
SOCPPortfolioConstraint .ConstraintViolationExceptionException thrown when a constraint is violated.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimizationSuanShu
SOCPPortfolioConstraint .VariableClasscom.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimizationSuanShu
SOCPPortfolioObjectiveFunctionConstructs the objective function for portfolio optimization.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimizationSuanShu
SOCPPortfolioProblemConstructs an SOCP problem for portfolio optimization.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimizationSuanShu
SOCPRiskConstraintClasscom.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimizationSuanShu
SQPActiveSetMinimizerSequential quadratic programming (SQP) is an iterative method for nonlinear optimization.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.general.sqp.activesetSuanShu
SQPActiveSetMinimizer .VariationFactoryInterfacecom.numericalmethod.suanshu.optimization.multivariate.constrained.general.sqp.activesetSuanShu
SQPActiveSetOnlyEqualityConstraint1MinimizerThis implementation is a modified version of Algorithm 15.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.general.sqp.activeset.equalityconstraintSuanShu
SQPActiveSetOnlyEqualityConstraint1Minimizer .VariationFactoryThis factory constructs a new instance of SQPASEVariation for each SQP problem.Interfacecom.numericalmethod.suanshu.optimization.multivariate.constrained.general.sqp.activeset.equalityconstraintSuanShu
SQPActiveSetOnlyEqualityConstraint2MinimizerThis particular implementation of SQPActiveSetOnlyEqualityConstraint1Minimizer uses SQPASEVariation2.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.general.sqp.activeset.equalityconstraintSuanShu
SQPActiveSetOnlyInequalityConstraintMinimizerThis implementation is a modified version of Algorithm 15.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.general.sqp.activesetSuanShu
SQPASEVariationThis interface allows customization of certain operations in the Active Set algorithm to solve a general constrained minimization problem with only equality constraints using Sequential Quadratic Programming.Interfacecom.numericalmethod.suanshu.optimization.multivariate.constrained.general.sqp.activeset.equalityconstraintSuanShu
SQPASEVariation1This implementation is a modified version of the algorithm in the reference to solve a general constrained minimization problem using Sequential Quadratic Programming.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.general.sqp.activeset.equalityconstraintSuanShu
SQPASEVariation2This implementation tries to find an exact positive definite Hessian whenever possible.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.general.sqp.activeset.equalityconstraintSuanShu
SQPASVariationThis interface allows customization of certain operations in the Active Set algorithm to solve a general constrained minimization problem using Sequential Quadratic Programming.Interfacecom.numericalmethod.suanshu.optimization.multivariate.constrained.general.sqp.activesetSuanShu
SQPASVariation1This implementation is a modified version of Algorithm 15.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.general.sqp.activesetSuanShu
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
SubProblemMinimizerThis minimizer solves a constrained optimization sub-problem where the values for some variables are held fixed for the original optimization problem.Classcom.numericalmethod.suanshu.optimization.multivariate.constrainedSuanShu
SubProblemMinimizer .ConstrainedMinimizerFactoryThis factory constructs a new instance of ConstrainedMinimizer to solve a real valued minimizationInterfacecom.numericalmethod.suanshu.optimization.multivariate.constrainedSuanShu
SubProblemMinimizer .IterativeSolutionGets the minimizer to the original problem.Interfacecom.numericalmethod.suanshu.optimization.multivariate.constrainedSuanShu
SumOfPenaltiesThis penalty function sums up the costs from a set of constituent penalty functions.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.general.penaltymethodSuanShu
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
UniformDistributionOverBox1This algorithm, by sampling uniformly in each dimension, generates a set of initials uniformly distributed over a box region,Classcom.numericalmethod.suanshu.optimization.multivariate.initializationSuanShu
UniformDistributionOverBox2This algorithm, by perturbing each grid point by a small random scale, generates a set of initials uniformly distributed over a box region,Classcom.numericalmethod.suanshu.optimization.multivariate.initializationSuanShu
UniformMeshOverRegionThe initial population is generated by putting a uniform mesh/grid/net over the entire region.Classcom.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.simplegrid.firstgenerationSuanShu
UnivariateMinimizerA univariate minimizer minimizes a univariate function.Interfacecom.numericalmethod.suanshu.optimization.univariateSuanShu
UnivariateMinimizer .SolutionThis is the solution to a univariate minimization problem.Interfacecom.numericalmethod.suanshu.optimization.univariateSuanShu
UpperBoundConstraintsClasscom.numericalmethod.suanshu.optimization.multivariate.constrained.constraint.linearSuanShu
ZangwillMinimizerZangwill's algorithm is an improved version of Powell's algorithm.Classcom.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.conjugatedirectionSuanShu
ZeroPenaltyThis is a dummy zero cost (no cost) penalty function.Classcom.numericalmethod.suanshu.optimization.multivariate.constrained.general.penaltymethodSuanShu