Name | Description | Type | Package | Framework |
AbsoluteErrorPenalty | This penalty function sums up the absolute error penalties. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.general.penaltymethod | SuanShu |
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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 |
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AntoniouLu2007 | This implementation is based on Algorithm 14. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.interiorpoint | SuanShu |
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Best1Bin | The 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. | Class | com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.deoptim | SuanShu |
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Best2Bin | The Best-1-Bin rule always picks the best chromosome as the base. | Class | com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.deoptim | SuanShu |
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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 |
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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 |
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BoltzTemperatureFunction | (T_k = T_0 / ln(k)). | Class | com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.temperaturefunction | SuanShu |
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BoxConstraints | This represents the lower and upper bounds for a variable. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.constraint.linear | SuanShu |
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BoxConstraints .Bound | A bound constraint for a variable. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.constraint.linear | SuanShu |
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BoxGeneralizedSimulatedAnnealingMinimizer | This is an extension to GeneralizedSimulatedAnnealingMinimizer, which allows adding box constraints to bound solutions. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.general.box | SuanShu |
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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 |
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BoxGSAAnnealingFunction | | Class | com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.annealingfunction | SuanShu |
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BoxMinimizer | A box minimizer solves a BoxOptimProblem. | Interface | com.numericalmethod.suanshu.optimization.multivariate.constrained | SuanShu |
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BoxOptimProblem | A box constrained optimization problem, for which a solution must be within fixed bounds. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.problem | SuanShu |
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BracketSearchMinimizer | This class provides implementation support for those univariate optimization algorithms that are based on bracketing. | Class | com.numericalmethod.suanshu.optimization.univariate.bracketsearch | SuanShu |
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BrentMinimizer | Brent's algorithm is the preferred method for finding the minimum of a univariate function. | Class | com.numericalmethod.suanshu.optimization.univariate.bracketsearch | SuanShu |
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BruteForceIPMinimizer | This implementation solves an integral constrained minimization problem by brute force search for all possible integer combinations. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.integer.bruteforce | SuanShu |
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BruteForceIPProblem | This implementation is an integral constrained minimization problem that has enumerable integral domains. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.integer.bruteforce | SuanShu |
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BruteForceIPProblem .IntegerDomain | This specifies the integral domain for an integral variable, i. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.integer.bruteforce | SuanShu |
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C2OptimProblem | This is an optimization problem of a real valued function that is twice differentiable. | Interface | com.numericalmethod.suanshu.optimization.problem | SuanShu |
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C2OptimProblemImpl | This is an optimization problem of a real valued function: (max_x f(x)). | Class | com.numericalmethod.suanshu.optimization.problem | SuanShu |
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CentralPath | A central path is a solution to both the primal and dual problems of a semi-definite programming problem. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.pathfollowing | SuanShu |
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Chromosome | A chromosome is a representation of a solution to an optimization problem. | Interface | com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm | SuanShu |
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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 |
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ConstrainedCellFactory | This defines a Differential Evolution operator that takes in account constraints. | Class | com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.deoptim.constrained | SuanShu |
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ConstrainedMinimizer | A constrained minimizer solves a constrained optimization problem, namely, ConstrainedOptimProblem. | Interface | com.numericalmethod.suanshu.optimization.multivariate.constrained | SuanShu |
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ConstrainedOptimProblem | A constrained optimization problem takes this form. | Interface | com.numericalmethod.suanshu.optimization.multivariate.constrained.problem | SuanShu |
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ConstrainedOptimProblemImpl1 | This implements a constrained optimization problem for a function f subject to equality and less-than-or-equal-to constraints. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.problem | SuanShu |
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ConstrainedOptimSubProblem | A constrained optimization sub-problem takes this form. | Interface | com.numericalmethod.suanshu.optimization.multivariate.constrained | SuanShu |
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Constraints | A set of constraints for a (real-valued) optimization problem is a set of functions. | Interface | com.numericalmethod.suanshu.optimization.multivariate.constrained.constraint | SuanShu |
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ConstraintsUtils | These are the utility functions for manipulating Constraints. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.constraint | SuanShu |
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CourantPenalty | This penalty function sums up the squared error penalties. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.general.penaltymethod | SuanShu |
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CSDPMinimizer | See Also:"Borchers, Brian, "CSDP, a C Library for Semidefinite Programming", Optimization Methods and Software 11(1): 613-623, 1999. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.pathfollowing | SuanShu |
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DefaultSimplex | A simplex optimization algorithm, e. | Class | com.numericalmethod.suanshu.optimization.multivariate.initialization | SuanShu |
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DEOptim | Differential Evolution (DE) is a global optimization method. | Class | com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.deoptim | SuanShu |
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DEOptim .NewCellFactory | This factory constructs a new DEOptimCellFactory for each minimization problem. | Interface | com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.deoptim | SuanShu |
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DEOptimCellFactory | A DEOptimCellFactory produces DEOptimCellFactory. | Class | com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.deoptim | SuanShu |
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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 |
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EqualityConstraints | The domain of an optimization problem may be restricted by equality constraints. | Interface | com.numericalmethod.suanshu.optimization.multivariate.constrained.constraint | SuanShu |
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ExpTemperatureFunction | Logarithmic decay, where (T_k = T_0 * 0. | Class | com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.temperaturefunction | SuanShu |
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FastAnnealingFunction | Matlab default: @annealingfast - The step has length temperature, with direction uniformly at random. | Class | com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.annealingfunction | SuanShu |
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FastTemperatureFunction | Linear decay, where (T_k = T_0 / k). | Class | com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.temperaturefunction | SuanShu |
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FerrisMangasarianWrightPhase1 | The phase 1 procedure finds a feasible table from an infeasible one by pivoting the simplex table of a related problem. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex | SuanShu |
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FerrisMangasarianWrightPhase2 | This implementation solves a canonical linear programming problem that does not need preprocessing its simplex table. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solver | SuanShu |
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FerrisMangasarianWrightScheme2 | The scheme 2 procedure removes equalities and free variables. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex | SuanShu |
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FibonaccMinimizer | The Fibonacci search is a dichotomous search where a bracketing interval is sub-divided by the Fibonacci ratio. | Class | com.numericalmethod.suanshu.optimization.univariate.bracketsearch | SuanShu |
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FirstGeneration | This interface allows customization of how the first pool of chromosomes is generated. | Interface | com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.simplegrid.firstgeneration | SuanShu |
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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 |
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FirstOrderMinimizer .Method | the available methods to do line searchThe line search is done analytically. | Class | com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.steepestdescent | SuanShu |
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FletcherLineSearch | This is Fletcher's inexact line search method. | Class | com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.linesearch | SuanShu |
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FletcherPenalty | This penalty function sums up the squared costs penalties. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.general.penaltymethod | SuanShu |
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FletcherReevesMinimizer | The Fletcher-Reeves method is a variant of the Conjugate-Gradient method. | Class | com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.conjugatedirection | SuanShu |
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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 |
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GaussNewtonMinimizer .MySteepestDescent | | Class | com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.steepestdescent | SuanShu |
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GeneralConstraints | The real-valued constraints define the domain (feasible regions) for a real-valued objective function in a constrained optimization problem. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.constraint.general | SuanShu |
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GeneralEqualityConstraints | This is the collection of equality constraints for an optimization problem. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.constraint.general | SuanShu |
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GeneralGreaterThanConstraints | This is the collection of greater-than-or-equal-to constraints for an optimization problem. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.constraint.general | SuanShu |
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GeneralizedSimulatedAnnealingMinimizer | Tsallis and Stariolo (1996) proposed this variant of SimulatedAnnealingMinimizer (SA). | Class | com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing | SuanShu |
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GeneralLessThanConstraints | This is the collection of less-than or equal-to constraints for an optimization problem. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.constraint.general | SuanShu |
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GeneticAlgorithm | A genetic algorithm (GA) is a search heuristic that mimics the process of natural evolution. | Class | com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm | SuanShu |
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GlobalSearchByLocalMinimizer | This minimizer is a global optimization method. | Class | com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.local | SuanShu |
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GoldenMinimizer | This is the golden section univariate minimization algorithm. | Class | com.numericalmethod.suanshu.optimization.univariate.bracketsearch | SuanShu |
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GomoryMixedCutMinimizer | This cutting-plane implementation uses Gomory's mixed cut method. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.integer.linear.cuttingplane | SuanShu |
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GomoryMixedCutMinimizer .MyCutter | This is Gomory's mixed cut. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.integer.linear.cuttingplane | SuanShu |
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GomoryPureCutMinimizer | This cutting-plane implementation uses Gomory's pure cut method for pure integer programming, in which all variables are integral. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.integer.linear.cuttingplane | SuanShu |
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GomoryPureCutMinimizer .MyCutter | This is Gomory's pure cut. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.integer.linear.cuttingplane | SuanShu |
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GreaterThanConstraints | The domain of an optimization problem may be restricted by greater-than or equal-to constraints. | Interface | com.numericalmethod.suanshu.optimization.multivariate.constrained.constraint | SuanShu |
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GridSearchMinimizer | This performs a grid search to find the minimum of a univariate function. | Class | com.numericalmethod.suanshu.optimization.univariate | SuanShu |
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GSAAcceptanceProbabilityFunction | The GSA acceptance probability function. | Class | com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.acceptanceprobabilityfunction | SuanShu |
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GSAAnnealingFunction | The GSA proposal/annealing function. | Class | com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.annealingfunction | SuanShu |
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GSATemperatureFunction | The GSA temperature function. | Class | com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.temperaturefunction | SuanShu |
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HomogeneousPathFollowingMinimizer | This implementation solves a Semi-Definite Programming problem using the Homogeneous Self-Dual Path-Following algorithm. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.pathfollowing | SuanShu |
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Hp | This is the symmetrization operator as defined in equation (6) in the reference. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.pathfollowing | SuanShu |
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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 |
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ILPBranchAndBoundMinimizer | This is a Branch-and-Bound algorithm that solves Integer Linear Programming problems. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.integer.linear.bb | SuanShu |
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ILPBranchAndBoundMinimizer .ActiveListFactory | This factory constructs a new instance of ActiveList for each Integer Linear Programming problem. | Interface | com.numericalmethod.suanshu.optimization.multivariate.constrained.integer.linear.bb | SuanShu |
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ILPNode | This is the branch-and-bound node used in conjunction with ILPBranchAndBoundMinimizer to solve an Integer Linear Programming problem. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.integer.linear.bb | SuanShu |
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ILPProblem | A linear program in real variables is said to be integral if it has at least one optimal solution which is integral. | Interface | com.numericalmethod.suanshu.optimization.multivariate.constrained.integer.linear.problem | SuanShu |
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ILPProblemImpl1 | This implementation is an ILP problem, in which the variables can be real or integral. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.integer.linear.problem | SuanShu |
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InitialsFactory | Some optimization algorithms, e. | Interface | com.numericalmethod.suanshu.optimization.multivariate.initialization | SuanShu |
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IntegralConstrainedCellFactory | This implementation defines the constrained Differential Evolution operators that solve an Integer Programming problem. | Class | com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.deoptim.constrained | SuanShu |
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IntegralConstrainedCellFactory .AllIntegers | This integral constraint makes all variables in the objective function integral variables. | Class | com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.deoptim.constrained | SuanShu |
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IntegralConstrainedCellFactory .IntegerConstraint | The integral constraints are defined by implementing this interface. | Interface | com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.deoptim.constrained | SuanShu |
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IntegralConstrainedCellFactory .SomeIntegers | This integral constraint makes some variables in the objective function integral variables. | Class | com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.deoptim.constrained | SuanShu |
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IPMinimizer | An Integer Programming minimizer minimizes an objective function subject to equality/inequality constraints as well as integral constraints. | Interface | com.numericalmethod.suanshu.optimization.multivariate.constrained.integer | SuanShu |
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IPProblem | An Integer Programming problem is a mathematical optimization or feasibility program in which some or all of the variables are restricted to be integers. | Interface | com.numericalmethod.suanshu.optimization.multivariate.constrained.integer | SuanShu |
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IPProblemImpl1 | This is an implementation of a general Integer Programming problem in which some variables take only integers. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.integer | SuanShu |
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IterativeC2Maximizer | A maximization problem is simply minimizing the negative of the objective function. | Class | com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2 | SuanShu |
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IterativeC2Maximizer .Solution | | Interface | com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2 | SuanShu |
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IterativeC2Minimizer | This is a minimizer that minimizes a twice continuously differentiable, multivariate function. | Interface | com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2 | SuanShu |
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IterativeMinimizer | This is an iterative multivariate minimizer. | Interface | com.numericalmethod.suanshu.optimization.multivariate.unconstrained | SuanShu |
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IterativeSolution | Many minimization algorithms work by starting from some given initials and iteratively moving toward an approximate solution. | Interface | com.numericalmethod.suanshu.optimization | SuanShu |
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JordanExchange | Jordan Exchange swaps the r-th entering variable (row) with the s-th leaving variable (column) in a matrix A. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex | SuanShu |
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LeastPth | The least p-th minmax algorithm minimizes the maximal error/loss (function): min_x max_{omega in S} e(x, omega) | Class | com.numericalmethod.suanshu.optimization.multivariate.minmax | SuanShu |
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LessThanConstraints | The domain of an optimization problem may be restricted by less-than or equal-to constraints. | Interface | com.numericalmethod.suanshu.optimization.multivariate.constrained.constraint | SuanShu |
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LinearConstraints | This is a collection of linear constraints for a real-valued optimization problem. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.constraint.linear | SuanShu |
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LinearEqualityConstraints | This is a collection of linear equality constraints. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.constraint.linear | SuanShu |
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LinearGreaterThanConstraints | This is a collection of linear greater-than-or-equal-to constraints. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.constraint.linear | SuanShu |
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LinearLessThanConstraints | This is a collection of linear less-than-or-equal-to constraints. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.constraint.linear | SuanShu |
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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 |
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LineSearch .Solution | This is the solution to a line search minimization. | Interface | com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.linesearch | SuanShu |
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LocalSearchCellFactory | A LocalSearchCellFactory produces LocalSearchCellFactory. | Class | com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.local | SuanShu |
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LocalSearchCellFactory .MinimizerFactory | This factory constructs a new Minimizer for each mutation operation. | Interface | com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.local | SuanShu |
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LowerBoundConstraints | | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.constraint.linear | SuanShu |
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LPBoundedMinimizer | This is the solution to a bounded linear programming problem. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solution | SuanShu |
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LPCanonicalProblem1 | | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.problem | SuanShu |
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LPCanonicalProblem2 | | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.problem | SuanShu |
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LPCanonicalSolver | This is an LP solver that solves a canonical LP problem in the following form. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solver | SuanShu |
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LPDimensionNotMatched | This is the exception thrown when the dimensions of the objective function and constraints of a linear programming problem are inconsistent. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.exception | SuanShu |
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LPEmptyCostVector | This is the exception thrown when there is no objective function in a linear programming problem. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.exception | SuanShu |
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LPException | This is the exception thrown when there is any problem when solving a linear programming problem. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.exception | SuanShu |
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LPInfeasible | This is the exception thrown when the LP problem is infeasible, i. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.exception | SuanShu |
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LPMinimizer | An LP minimizer minimizes the objective of an LP problem, satisfying all the constraints. | Interface | com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp | SuanShu |
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LPNoConstraint | This is the exception thrown when there is no linear constraint found for the LP problem. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.exception | SuanShu |
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LPProblem | A linear programming (LP) problem minimizes a linear objective function subject to a collection of linear constraints. | Interface | com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.problem | SuanShu |
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LPProblemImpl1 | This is an implementation of a linear programming problem, LPProblem. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.problem | SuanShu |
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LPRuntimeException | This is the exception thrown when there is any problem when constructing a linear programming problem. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.exception | SuanShu |
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LPSimplexMinimizer | A simplex LP minimizer can be read off from the solution simplex table. | Interface | com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solution | SuanShu |
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LPSimplexSolution | The solution to a linear programming problem using a simplex method contains an LPSimplexMinimizer. | Interface | com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solution | SuanShu |
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LPSimplexSolver | A simplex solver works toward an LP solution by sequentially applying Jordan exchange to a simplex table. | Interface | com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solver | SuanShu |
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LPSolution | A 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. | Interface | com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp | SuanShu |
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LPSolver | An LP solver solves a Linear Programming (LP) problem. | Interface | com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp | SuanShu |
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LPStandardProblem | | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.problem | SuanShu |
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LPTwoPhaseSolver | This implementation solves a linear programming problem, LPProblem, using a two-step approach. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solver | SuanShu |
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LPUnbounded | This is the exception thrown when the LP problem is unbounded. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.exception | SuanShu |
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LPUnboundedMinimizer | This is the solution to an unbounded linear programming problem. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solution | SuanShu |
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LPUnboundedMinimizerScheme2 | This is the solution to an unbounded linear programming problem found in scheme 2. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solution | SuanShu |
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MaximizationSolution | This is the solution to a maximization problem. | Interface | com.numericalmethod.suanshu.optimization | SuanShu |
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Maxmizer | This interface represents an optimization algorithm that maximizers a real valued objective function, one or multi dimension. | Interface | com.numericalmethod.suanshu.optimization | SuanShu |
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McCormickMinimizer | This is the McCormick method. | Class | com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.quasinewton | SuanShu |
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MetropolisAcceptanceProbabilityFunction | Uses the classic Metropolis rule, f_{t+1}/f_t. | Class | com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.acceptanceprobabilityfunction | SuanShu |
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MinimizationSolution | This is the solution to a minimization problem. | Interface | com.numericalmethod.suanshu.optimization | SuanShu |
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Minimizer | This interface represents an optimization algorithm that minimizes a real valued objective function, one or multi dimension. | Interface | com.numericalmethod.suanshu.optimization | SuanShu |
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MinMaxMinimizer | A minmax minimizer minimizes a minmax problem. | Interface | com.numericalmethod.suanshu.optimization.multivariate.minmax | SuanShu |
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MultiplierPenalty | A multiplier penalty function allows different weights to be assigned to the constraints. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.general.penaltymethod | SuanShu |
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MultivariateMinimizer | This is a minimizer that minimizes a multivariate function or a Vector function. | Interface | com.numericalmethod.suanshu.optimization.multivariate.unconstrained | SuanShu |
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NaiveRule | This pivoting rule chooses the column with the most negative reduced cost. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.pivoting | SuanShu |
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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 |
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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 |
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NonNegativityConstraintOptimProblem | This is a constrained optimization problem for a function which has all non-negative variables. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.problem | SuanShu |
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NonNegativityConstraints | These constraints ensures that for all variables are non-negative. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.constraint.linear | SuanShu |
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Optimizer | Optimization, or mathematical programming, refers to choosing the best element from some set of available alternatives. | Interface | com.numericalmethod.suanshu.optimization | SuanShu |
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OptimProblem | This is an optimization problem that minimizes a real valued objective function, one or multi dimension. | Interface | com.numericalmethod.suanshu.optimization.problem | SuanShu |
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PearsonMinimizer | This is the Pearson method. | Class | com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.quasinewton | SuanShu |
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PenaltyFunction | A function P: Rn -> R is a penalty function for a constrained optimization problem if it has these properties. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.general.penaltymethod | SuanShu |
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PenaltyMethodMinimizer | The penalty method is an algorithm for solving a constrained minimization problem with general It replaces a constrained optimization problem by a series of unconstrained problems | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.general.penaltymethod | SuanShu |
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PenaltyMethodMinimizer .PenaltyFunctionFactory | For each constrained optimization problem, the solver creates a new penalty function for it. | Interface | com.numericalmethod.suanshu.optimization.multivariate.constrained.general.penaltymethod | SuanShu |
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PerturbationAroundPoint | The initial population is generated by adding a variance around a given initial. | Class | com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.simplegrid.firstgeneration | SuanShu |
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PortfolioRiskExactSigma .DefaultRoot | Computes the matrix root by Cholesky and on failure by MatrixRootByDiagonalization. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization | SuanShu |
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PortfolioRiskExactSigma .Diagonalization | Computes the matrix root by MatrixRootByDiagonalization. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization | SuanShu |
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PortfolioRiskExactSigma .MatrixRoot | Specifies the method to compute the root of a matrix. | Interface | com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization | SuanShu |
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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 |
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PrimalDualInteriorPointMinimizer | Solves 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. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.interiorpoint | SuanShu |
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PrimalDualPathFollowingMinimizer | The Primal-Dual Path-Following algorithm is an interior point method that solves Semi-Definite Programming problems. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.pathfollowing | SuanShu |
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PrimalDualSolution | The vector set {x, s, y} is a solution to both the primal and dual SOCP problems. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.interiorpoint | SuanShu |
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PureILPProblem | This is a pure integer linear programming problem, in which all variables are integral. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.integer.linear.problem | SuanShu |
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QPDualActiveSetMinimizer | This implementation solves a Quadratic Programming problem using the dual active set algorithm. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.activeset | SuanShu |
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QPException | This is the exception thrown when there is an error solving a quadratic programming problem. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp | SuanShu |
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QPInfeasible | This is the exception thrown by a quadratic programming solver when the quadratic programming problem is infeasible, i. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp | SuanShu |
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QPPrimalActiveSetMinimizer | This implementation solves a Quadratic Programming problem using the Primal Active Set algorithm. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.activeset | SuanShu |
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QPProblem | Quadratic Programming is the problem of optimizing (minimizing) a quadratic function of several variables subject to linear constraints on these variables. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.problem | SuanShu |
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QPProblemOnlyEqualityConstraints | A quadratic programming problem with only equality constraints can be converted into a equivalent quadratic programming problem without constraints, hence a mere quadratic function. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.problem | SuanShu |
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QPSimpleMinimizer | These are the utility functions to solve simple quadratic programming problems that admit analytical solutions. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp | SuanShu |
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QPSolution | This is a solution to a quadratic programming problem. | Interface | com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp | SuanShu |
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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 |
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Rand1Bin | The Rand-1-Bin rule is defined by: mutation by adding a scaled, randomly sampled vector difference to a third vector | Class | com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.deoptim | SuanShu |
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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 |
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RealScalarFunctionChromosome | This chromosome encodes a real valued function. | Class | com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.simplegrid | SuanShu |
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SDPDualProblem | A dual SDP problem, as in equation 14. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.problem | SuanShu |
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SDPDualProblem .EqualityConstraints | This is the collection of equality constraints: sum_{i=1}^{p}y_imathbf{A_i}+ extbf{S} = extbf{C}, extbf{S} succeq extbf{0} | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.problem | SuanShu |
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SDPPrimalProblem | A Primal SDP problem, as in equation 14. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.problem | SuanShu |
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SDPT3v4 | This implements Algorithm_IPC, the SOCP interior point algorithm in SDPT3 version 4. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.interiorpoint | SuanShu |
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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 |
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SimpleCellFactory | A SimpleCellFactory produces SimpleCellFactory. | Class | com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.simplegrid | SuanShu |
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SimpleGridMinimizer | This minimizer is a simple global optimization method. | Class | com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.simplegrid | SuanShu |
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SimpleGridMinimizer .NewCellFactoryCtor | This factory constructs a new SimpleCellFactory for each minimization problem. | Interface | com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.simplegrid | SuanShu |
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SimpleTemperatureFunction | Abstract class for the common case where (T^V_t = T^A_t). | Class | com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.temperaturefunction | SuanShu |
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SimplexCuttingPlaneMinimizer | The use of cutting planes to solve Mixed Integer Linear Programming (MILP) problems was introduced by Ralph E Gomory. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.integer.linear.cuttingplane | SuanShu |
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SimplexCuttingPlaneMinimizer .CutterFactory | This factory constructs a new Cutter for each MILP problem. | Interface | com.numericalmethod.suanshu.optimization.multivariate.constrained.integer.linear.cuttingplane | SuanShu |
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SimplexCuttingPlaneMinimizer .CutterFactory .Cutter | A Cutter defines how to cut a simplex table, i. | Interface | com.numericalmethod.suanshu.optimization.multivariate.constrained.integer.linear.cuttingplane | SuanShu |
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SimplexPivoting | A simplex pivoting finds a row and column to exchange to reduce the cost function. | Interface | com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.pivoting | SuanShu |
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SimplexPivoting .Pivot | | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.pivoting | SuanShu |
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SimplexTable | This is a simplex table used to solve a linear programming problem using a simplex method. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex | SuanShu |
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SimplexTable .Label | | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex | SuanShu |
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SimplexTable .LabelType | the artificial variable, x0, pp. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex | SuanShu |
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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 |
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SmallestSubscriptRule | Bland's smallest-subscript rule is for anti-cycling in choosing a pivot. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.pivoting | SuanShu |
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SOCPDualProblem | This is the Dual Second Order Conic Programming problem. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem | SuanShu |
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SOCPDualProblem .EqualityConstraints | | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem | SuanShu |
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SOCPGeneralConstraint | This represents the SOCP general constraint of this form. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem | SuanShu |
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SOCPGeneralConstraints | This represents a set of SOCP general constraints of this form. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem | SuanShu |
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SOCPGeneralProblem | Many convex programming problems can be represented in the following form. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem | SuanShu |
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SOCPPortfolioConstraint | An SOCP constraint for portfolio optimization, e. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization | SuanShu |
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SOCPPortfolioConstraint .ConstraintViolationException | Exception thrown when a constraint is violated. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization | SuanShu |
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SOCPPortfolioConstraint .Variable | | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization | SuanShu |
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SOCPPortfolioObjectiveFunction | Constructs the objective function for portfolio optimization. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization | SuanShu |
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SOCPPortfolioProblem | Constructs an SOCP problem for portfolio optimization. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization | SuanShu |
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SOCPRiskConstraint | | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization | SuanShu |
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SQPActiveSetMinimizer | Sequential quadratic programming (SQP) is an iterative method for nonlinear optimization. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.general.sqp.activeset | SuanShu |
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SQPActiveSetMinimizer .VariationFactory | | Interface | com.numericalmethod.suanshu.optimization.multivariate.constrained.general.sqp.activeset | SuanShu |
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SQPActiveSetOnlyEqualityConstraint1Minimizer | This implementation is a modified version of Algorithm 15. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.general.sqp.activeset.equalityconstraint | SuanShu |
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SQPActiveSetOnlyEqualityConstraint1Minimizer .VariationFactory | This factory constructs a new instance of SQPASEVariation for each SQP problem. | Interface | com.numericalmethod.suanshu.optimization.multivariate.constrained.general.sqp.activeset.equalityconstraint | SuanShu |
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SQPActiveSetOnlyEqualityConstraint2Minimizer | This particular implementation of SQPActiveSetOnlyEqualityConstraint1Minimizer uses SQPASEVariation2. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.general.sqp.activeset.equalityconstraint | SuanShu |
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SQPActiveSetOnlyInequalityConstraintMinimizer | This implementation is a modified version of Algorithm 15. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.general.sqp.activeset | SuanShu |
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SQPASEVariation | This 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. | Interface | com.numericalmethod.suanshu.optimization.multivariate.constrained.general.sqp.activeset.equalityconstraint | SuanShu |
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SQPASEVariation1 | This implementation is a modified version of the algorithm in the reference to solve a general constrained minimization problem using Sequential Quadratic Programming. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.general.sqp.activeset.equalityconstraint | SuanShu |
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SQPASEVariation2 | This implementation tries to find an exact positive definite Hessian whenever possible. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.general.sqp.activeset.equalityconstraint | SuanShu |
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SQPASVariation | This interface allows customization of certain operations in the Active Set algorithm to solve a general constrained minimization problem using Sequential Quadratic Programming. | Interface | com.numericalmethod.suanshu.optimization.multivariate.constrained.general.sqp.activeset | SuanShu |
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SQPASVariation1 | This implementation is a modified version of Algorithm 15. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.general.sqp.activeset | SuanShu |
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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 |
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SubProblemMinimizer | This minimizer solves a constrained optimization sub-problem where the values for some variables are held fixed for the original optimization problem. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained | SuanShu |
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SubProblemMinimizer .ConstrainedMinimizerFactory | This factory constructs a new instance of ConstrainedMinimizer to solve a real valued minimization | Interface | com.numericalmethod.suanshu.optimization.multivariate.constrained | SuanShu |
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SubProblemMinimizer .IterativeSolution | Gets the minimizer to the original problem. | Interface | com.numericalmethod.suanshu.optimization.multivariate.constrained | SuanShu |
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SumOfPenalties | This penalty function sums up the costs from a set of constituent penalty functions. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.general.penaltymethod | SuanShu |
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TemperatureFunction | A temperature function defines a temperature schedule used in simulated annealing. | Interface | com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.temperaturefunction | SuanShu |
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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 |
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UniformDistributionOverBox1 | This algorithm, by sampling uniformly in each dimension, generates a set of initials uniformly distributed over a box region, | Class | com.numericalmethod.suanshu.optimization.multivariate.initialization | SuanShu |
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UniformDistributionOverBox2 | This algorithm, by perturbing each grid point by a small random scale, generates a set of initials uniformly distributed over a box region, | Class | com.numericalmethod.suanshu.optimization.multivariate.initialization | SuanShu |
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UniformMeshOverRegion | The initial population is generated by putting a uniform mesh/grid/net over the entire region. | Class | com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.simplegrid.firstgeneration | SuanShu |
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UnivariateMinimizer | A univariate minimizer minimizes a univariate function. | Interface | com.numericalmethod.suanshu.optimization.univariate | SuanShu |
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UnivariateMinimizer .Solution | This is the solution to a univariate minimization problem. | Interface | com.numericalmethod.suanshu.optimization.univariate | SuanShu |
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UpperBoundConstraints | | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.constraint.linear | SuanShu |
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ZangwillMinimizer | Zangwill's algorithm is an improved version of Powell's algorithm. | Class | com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.conjugatedirection | SuanShu |
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ZeroPenalty | This is a dummy zero cost (no cost) penalty function. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.general.penaltymethod | SuanShu |