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 |
|
AntoniouLu2007 | This implementation is based on Algorithm 14. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.interiorpoint | 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 |
|
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 |
|
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 |
|
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 |
|
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 |
|
EqualityConstraints | The domain of an optimization problem may be restricted by equality constraints. | Interface | com.numericalmethod.suanshu.optimization.multivariate.constrained.constraint | 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 |
|
FletcherPenalty | This penalty function sums up the squared costs penalties. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.general.penaltymethod | 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 |
|
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 |
|
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 |
|
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 |
|
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 |
|
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 |
|
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 |
|
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 |
|
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 |
|
MultiplierPenalty | A multiplier penalty function allows different weights to be assigned to the constraints. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.general.penaltymethod | 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 |
|
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 |
|
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 |
|
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 |
|
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 |
|
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 |
|
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 |
|
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 |
|
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 |
|
UpperBoundConstraints | | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.constraint.linear | SuanShu |
|
ZeroPenalty | This is a dummy zero cost (no cost) penalty function. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.general.penaltymethod | SuanShu |