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#Com.numericalmethod.suanshu.optimization Classes and Interfaces - 225 results found.
| 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 |
| 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 |
| AntoniouLu2007 | This implementation is based on Algorithm 14. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.interiorpoint | SuanShu |
| 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 |
| Best2Bin | The Best-1-Bin rule always picks the best chromosome as the base. | Class | com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.deoptim | SuanShu |
| BFGSMinimizer | The Broyden-Fletcher-Goldfarb-Shanno method is a quasi-Newton method to solve unconstrained nonlinear optimization problems. | Class | com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.quasinewton | SuanShu |
| BoltzAnnealingFunction | Matlab: @annealingboltz - The step has length square root of temperature, with direction uniformly at random. | Class | com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.annealingfunction | SuanShu |
| BoltzTemperatureFunction | (T_k = T_0 / ln(k)). | Class | com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.temperaturefunction | SuanShu |
| BoxConstraints | This represents the lower and upper bounds for a variable. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.constraint.linear | SuanShu |
| BoxConstraints .Bound | A bound constraint for a variable. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.constraint.linear | SuanShu |
| 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 |
| BoxGSAAcceptanceProbabilityFunction | This probability function boxes an unconstrained probability function so that when a proposed state is outside the box, it has a probability of 0. | Class | com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.acceptanceprobabilityfunction | SuanShu |
| BoxGSAAnnealingFunction | Class | com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.annealingfunction | SuanShu | |
| BoxMinimizer | A box minimizer solves a BoxOptimProblem. | Interface | com.numericalmethod.suanshu.optimization.multivariate.constrained | SuanShu |
| BoxOptimProblem | A box constrained optimization problem, for which a solution must be within fixed bounds. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.problem | SuanShu |
| BracketSearchMinimizer | This class provides implementation support for those univariate optimization algorithms that are based on bracketing. | Class | com.numericalmethod.suanshu.optimization.univariate.bracketsearch | SuanShu |
| BrentMinimizer | Brent's algorithm is the preferred method for finding the minimum of a univariate function. | Class | com.numericalmethod.suanshu.optimization.univariate.bracketsearch | SuanShu |
| 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 |
| BruteForceIPProblem | This implementation is an integral constrained minimization problem that has enumerable integral domains. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.integer.bruteforce | SuanShu |
| BruteForceIPProblem .IntegerDomain | This specifies the integral domain for an integral variable, i. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.integer.bruteforce | SuanShu |
| C2OptimProblem | This is an optimization problem of a real valued function that is twice differentiable. | Interface | com.numericalmethod.suanshu.optimization.problem | SuanShu |
| C2OptimProblemImpl | This is an optimization problem of a real valued function: (max_x f(x)). | Class | com.numericalmethod.suanshu.optimization.problem | SuanShu |
| 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 |
| Chromosome | A chromosome is a representation of a solution to an optimization problem. | Interface | com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm | SuanShu |
| ConjugateGradientMinimizer | A conjugate direction optimization method is performed by using sequential line search along directions that bear a strict mathematical relationship to one another. | Class | com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.conjugatedirection | SuanShu |
| ConstrainedCellFactory | This defines a Differential Evolution operator that takes in account constraints. | Class | com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.deoptim.constrained | SuanShu |
| ConstrainedMinimizer | A constrained minimizer solves a constrained optimization problem, namely, ConstrainedOptimProblem. | Interface | com.numericalmethod.suanshu.optimization.multivariate.constrained | SuanShu |
| ConstrainedOptimProblem | A constrained optimization problem takes this form. | Interface | com.numericalmethod.suanshu.optimization.multivariate.constrained.problem | SuanShu |
| 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 |
| ConstrainedOptimSubProblem | A constrained optimization sub-problem takes this form. | Interface | com.numericalmethod.suanshu.optimization.multivariate.constrained | SuanShu |
| 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 |
| ConstraintsUtils | These are the utility functions for manipulating Constraints. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.constraint | SuanShu |
| CourantPenalty | This penalty function sums up the squared error penalties. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.general.penaltymethod | SuanShu |
| 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 |
| DefaultSimplex | A simplex optimization algorithm, e. | Class | com.numericalmethod.suanshu.optimization.multivariate.initialization | SuanShu |
| DEOptim | Differential Evolution (DE) is a global optimization method. | Class | com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.deoptim | SuanShu |
| DEOptim .NewCellFactory | This factory constructs a new DEOptimCellFactory for each minimization problem. | Interface | com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.deoptim | SuanShu |
| DEOptimCellFactory | A DEOptimCellFactory produces DEOptimCellFactory. | Class | com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.deoptim | SuanShu |
| DFPMinimizer | The Davidon-Fletcher-Powell method is a quasi-Newton method to solve unconstrained nonlinear optimization problems. | Class | com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.quasinewton | SuanShu |
| EqualityConstraints | The domain of an optimization problem may be restricted by equality constraints. | Interface | com.numericalmethod.suanshu.optimization.multivariate.constrained.constraint | SuanShu |
| ExpTemperatureFunction | Logarithmic decay, where (T_k = T_0 * 0. | Class | com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.temperaturefunction | SuanShu |
| FastAnnealingFunction | Matlab default: @annealingfast - The step has length temperature, with direction uniformly at random. | Class | com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.annealingfunction | SuanShu |
| FastTemperatureFunction | Linear decay, where (T_k = T_0 / k). | Class | com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.temperaturefunction | SuanShu |
| 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 |
| 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 |
| FerrisMangasarianWrightScheme2 | The scheme 2 procedure removes equalities and free variables. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex | SuanShu |
| 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 |
| 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 |
| FirstOrderMinimizer | This implements the steepest descent line search using the first order expansion of the Taylor's series. | Class | com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.steepestdescent | SuanShu |
| FirstOrderMinimizer .Method | the available methods to do line searchThe line search is done analytically. | Class | com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.steepestdescent | SuanShu |
| FletcherLineSearch | This is Fletcher's inexact line search method. | Class | com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.linesearch | SuanShu |
| FletcherPenalty | This penalty function sums up the squared costs penalties. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.general.penaltymethod | SuanShu |
| FletcherReevesMinimizer | The Fletcher-Reeves method is a variant of the Conjugate-Gradient method. | Class | com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.conjugatedirection | SuanShu |
| GaussNewtonMinimizer | The Gauss-Newton method is a steepest descent method to minimize a real vector function in the form: f(x) = [f_1(x), f_2(x), . | Class | com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.steepestdescent | SuanShu |
| GaussNewtonMinimizer .MySteepestDescent | Class | com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.steepestdescent | SuanShu | |
| 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 |
| GeneralEqualityConstraints | This is the collection of equality constraints for an optimization problem. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.constraint.general | SuanShu |
| 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 |
| GeneralizedSimulatedAnnealingMinimizer | Tsallis and Stariolo (1996) proposed this variant of SimulatedAnnealingMinimizer (SA). | Class | com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing | SuanShu |
| 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 |
| GeneticAlgorithm | A genetic algorithm (GA) is a search heuristic that mimics the process of natural evolution. | Class | com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm | SuanShu |
| GlobalSearchByLocalMinimizer | This minimizer is a global optimization method. | Class | com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.local | SuanShu |
| GoldenMinimizer | This is the golden section univariate minimization algorithm. | Class | com.numericalmethod.suanshu.optimization.univariate.bracketsearch | SuanShu |
| GomoryMixedCutMinimizer | This cutting-plane implementation uses Gomory's mixed cut method. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.integer.linear.cuttingplane | SuanShu |
| GomoryMixedCutMinimizer .MyCutter | This is Gomory's mixed cut. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.integer.linear.cuttingplane | SuanShu |
| 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 |
| GomoryPureCutMinimizer .MyCutter | This is Gomory's pure cut. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.integer.linear.cuttingplane | SuanShu |
| 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 |
| GridSearchMinimizer | This performs a grid search to find the minimum of a univariate function. | Class | com.numericalmethod.suanshu.optimization.univariate | SuanShu |
| GSAAcceptanceProbabilityFunction | The GSA acceptance probability function. | Class | com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.acceptanceprobabilityfunction | SuanShu |
| GSAAnnealingFunction | The GSA proposal/annealing function. | Class | com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.annealingfunction | SuanShu |
| GSATemperatureFunction | The GSA temperature function. | Class | com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.temperaturefunction | SuanShu |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| InitialsFactory | Some optimization algorithms, e. | Interface | com.numericalmethod.suanshu.optimization.multivariate.initialization | SuanShu |
| 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 |
| 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 |
| IntegralConstrainedCellFactory .IntegerConstraint | The integral constraints are defined by implementing this interface. | Interface | com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.deoptim.constrained | SuanShu |
| 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 |
| 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 |
| 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 |
| 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 |
| IterativeC2Maximizer | A maximization problem is simply minimizing the negative of the objective function. | Class | com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2 | SuanShu |
| IterativeC2Maximizer .Solution | Interface | com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2 | SuanShu | |
| IterativeC2Minimizer | This is a minimizer that minimizes a twice continuously differentiable, multivariate function. | Interface | com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2 | SuanShu |
| IterativeMinimizer | This is an iterative multivariate minimizer. | Interface | com.numericalmethod.suanshu.optimization.multivariate.unconstrained | SuanShu |
| IterativeSolution | Many minimization algorithms work by starting from some given initials and iteratively moving toward an approximate solution. | Interface | com.numericalmethod.suanshu.optimization | SuanShu |
| 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 |
| 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 |
| 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 |
| LinearConstraints | This is a collection of linear constraints for a real-valued optimization problem. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.constraint.linear | SuanShu |
| LinearEqualityConstraints | This is a collection of linear equality constraints. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.constraint.linear | SuanShu |
| LinearGreaterThanConstraints | This is a collection of linear greater-than-or-equal-to constraints. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.constraint.linear | SuanShu |
| LinearLessThanConstraints | This is a collection of linear less-than-or-equal-to constraints. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.constraint.linear | SuanShu |
| LineSearch | A line search is often used in another minimization algorithm to improve the current solution in one iteration step. | Interface | com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.linesearch | SuanShu |
| LineSearch .Solution | This is the solution to a line search minimization. | Interface | com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.linesearch | SuanShu |
| LocalSearchCellFactory | A LocalSearchCellFactory produces LocalSearchCellFactory. | Class | com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.local | SuanShu |
| LocalSearchCellFactory .MinimizerFactory | This factory constructs a new Minimizer for each mutation operation. | Interface | com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.local | SuanShu |
| LowerBoundConstraints | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.constraint.linear | SuanShu | |
| 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 |
| LPCanonicalProblem1 | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.problem | SuanShu | |
| LPCanonicalProblem2 | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.problem | SuanShu | |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| LPSolver | An LP solver solves a Linear Programming (LP) problem. | Interface | com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp | SuanShu |
| LPStandardProblem | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.problem | SuanShu | |
| 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 |
| 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 |
| 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 |
| 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 |
| MaximizationSolution | This is the solution to a maximization problem. | Interface | com.numericalmethod.suanshu.optimization | SuanShu |
| Maxmizer | This interface represents an optimization algorithm that maximizers a real valued objective function, one or multi dimension. | Interface | com.numericalmethod.suanshu.optimization | SuanShu |
| McCormickMinimizer | This is the McCormick method. | Class | com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.quasinewton | SuanShu |
| MetropolisAcceptanceProbabilityFunction | Uses the classic Metropolis rule, f_{t+1}/f_t. | Class | com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.acceptanceprobabilityfunction | SuanShu |
| MinimizationSolution | This is the solution to a minimization problem. | Interface | com.numericalmethod.suanshu.optimization | SuanShu |
| Minimizer | This interface represents an optimization algorithm that minimizes a real valued objective function, one or multi dimension. | Interface | com.numericalmethod.suanshu.optimization | SuanShu |
| MinMaxMinimizer | A minmax minimizer minimizes a minmax problem. | Interface | com.numericalmethod.suanshu.optimization.multivariate.minmax | SuanShu |
| MultiplierPenalty | A multiplier penalty function allows different weights to be assigned to the constraints. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.general.penaltymethod | SuanShu |
| MultivariateMinimizer | This is a minimizer that minimizes a multivariate function or a Vector function. | Interface | com.numericalmethod.suanshu.optimization.multivariate.unconstrained | SuanShu |
| 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 |
| NelderMeadMinimizer | The Nelder-Mead method is a nonlinear optimization technique, which is well-defined for twice differentiable and unimodal problems. | Class | com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2 | SuanShu |
| NewtonRaphsonMinimizer | The Newton-Raphson method is a second order steepest descent method that is based on the quadratic approximation of the Taylor series. | Class | com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.steepestdescent | SuanShu |
| 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 |
| NonNegativityConstraints | These constraints ensures that for all variables are non-negative. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.constraint.linear | SuanShu |
| Optimizer | Optimization, or mathematical programming, refers to choosing the best element from some set of available alternatives. | Interface | com.numericalmethod.suanshu.optimization | SuanShu |
| OptimProblem | This is an optimization problem that minimizes a real valued objective function, one or multi dimension. | Interface | com.numericalmethod.suanshu.optimization.problem | SuanShu |
| PearsonMinimizer | This is the Pearson method. | Class | com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.quasinewton | SuanShu |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| PortfolioRiskExactSigma .Diagonalization | Computes the matrix root by MatrixRootByDiagonalization. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization | SuanShu |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| QPSolution | This is a solution to a quadratic programming problem. | Interface | com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp | SuanShu |
| QuasiNewtonMinimizer | The Quasi-Newton methods in optimization are for finding local maxima and minima of functions. | Class | com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.quasinewton | SuanShu |
| 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 |
| 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 |
| RealScalarFunctionChromosome | This chromosome encodes a real valued function. | Class | com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.simplegrid | SuanShu |
| SDPDualProblem | A dual SDP problem, as in equation 14. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.problem | SuanShu |
| 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 |
| SDPPrimalProblem | A Primal SDP problem, as in equation 14. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.problem | SuanShu |
| 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 |
| 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 |
| SimpleCellFactory | A SimpleCellFactory produces SimpleCellFactory. | Class | com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.simplegrid | SuanShu |
| SimpleGridMinimizer | This minimizer is a simple global optimization method. | Class | com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.simplegrid | SuanShu |
| SimpleGridMinimizer .NewCellFactoryCtor | This factory constructs a new SimpleCellFactory for each minimization problem. | Interface | com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.simplegrid | SuanShu |
| SimpleTemperatureFunction | Abstract class for the common case where (T^V_t = T^A_t). | Class | com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.temperaturefunction | SuanShu |
| 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 |
| SimplexCuttingPlaneMinimizer .CutterFactory | This factory constructs a new Cutter for each MILP problem. | Interface | com.numericalmethod.suanshu.optimization.multivariate.constrained.integer.linear.cuttingplane | SuanShu |
| SimplexCuttingPlaneMinimizer .CutterFactory .Cutter | A Cutter defines how to cut a simplex table, i. | Interface | com.numericalmethod.suanshu.optimization.multivariate.constrained.integer.linear.cuttingplane | SuanShu |
| 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 |
| SimplexPivoting .Pivot | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.pivoting | SuanShu | |
| 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 |
| SimplexTable .Label | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex | SuanShu | |
| SimplexTable .LabelType | the artificial variable, x0, pp. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex | SuanShu |
| SimulatedAnnealingMinimizer | Simulated Annealing is a global optimization meta-heuristic that is inspired by annealing in metallurgy. | Class | com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing | SuanShu |
| 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 |
| SOCPDualProblem | This is the Dual Second Order Conic Programming problem. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem | SuanShu |
| SOCPDualProblem .EqualityConstraints | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem | SuanShu | |
| SOCPGeneralConstraint | This represents the SOCP general constraint of this form. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem | SuanShu |
| SOCPGeneralConstraints | This represents a set of SOCP general constraints of this form. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem | SuanShu |
| SOCPGeneralProblem | Many convex programming problems can be represented in the following form. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem | SuanShu |
| SOCPPortfolioConstraint | An SOCP constraint for portfolio optimization, e. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization | SuanShu |
| SOCPPortfolioConstraint .ConstraintViolationException | Exception thrown when a constraint is violated. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization | SuanShu |
| SOCPPortfolioConstraint .Variable | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization | SuanShu | |
| SOCPPortfolioObjectiveFunction | Constructs the objective function for portfolio optimization. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization | SuanShu |
| SOCPPortfolioProblem | Constructs an SOCP problem for portfolio optimization. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization | SuanShu |
| SOCPRiskConstraint | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization | SuanShu | |
| SQPActiveSetMinimizer | Sequential quadratic programming (SQP) is an iterative method for nonlinear optimization. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.general.sqp.activeset | SuanShu |
| SQPActiveSetMinimizer .VariationFactory | Interface | com.numericalmethod.suanshu.optimization.multivariate.constrained.general.sqp.activeset | SuanShu | |
| SQPActiveSetOnlyEqualityConstraint1Minimizer | This implementation is a modified version of Algorithm 15. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.general.sqp.activeset.equalityconstraint | SuanShu |
| 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 |
| SQPActiveSetOnlyEqualityConstraint2Minimizer | This particular implementation of SQPActiveSetOnlyEqualityConstraint1Minimizer uses SQPASEVariation2. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.general.sqp.activeset.equalityconstraint | SuanShu |
| SQPActiveSetOnlyInequalityConstraintMinimizer | This implementation is a modified version of Algorithm 15. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.general.sqp.activeset | SuanShu |
| 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 |
| 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 |
| 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 |
| 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 |
| SQPASVariation1 | This implementation is a modified version of Algorithm 15. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.general.sqp.activeset | SuanShu |
| SteepestDescentMinimizer | A steepest descent algorithm finds the minimum by moving along the negative of the steepest gradient direction. | Class | com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.steepestdescent | SuanShu |
| 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 |
| SubProblemMinimizer .ConstrainedMinimizerFactory | This factory constructs a new instance of ConstrainedMinimizer to solve a real valued minimization | Interface | com.numericalmethod.suanshu.optimization.multivariate.constrained | SuanShu |
| SubProblemMinimizer .IterativeSolution | Gets the minimizer to the original problem. | Interface | com.numericalmethod.suanshu.optimization.multivariate.constrained | SuanShu |
| 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 |
| TemperatureFunction | A temperature function defines a temperature schedule used in simulated annealing. | Interface | com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.temperaturefunction | SuanShu |
| TemperedAcceptanceProbabilityFunction | A tempered acceptance probability function computes the probability that the next state transition will be accepted. | Interface | com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.acceptanceprobabilityfunction | SuanShu |
| 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 |
| 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 |
| 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 |
| UnivariateMinimizer | A univariate minimizer minimizes a univariate function. | Interface | com.numericalmethod.suanshu.optimization.univariate | SuanShu |
| UnivariateMinimizer .Solution | This is the solution to a univariate minimization problem. | Interface | com.numericalmethod.suanshu.optimization.univariate | SuanShu |
| UpperBoundConstraints | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.constraint.linear | SuanShu | |
| ZangwillMinimizer | Zangwill's algorithm is an improved version of Powell's algorithm. | Class | com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.conjugatedirection | SuanShu |
| ZeroPenalty | This is a dummy zero cost (no cost) penalty function. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.general.penaltymethod | SuanShu |