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#Com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp Classes and Interfaces - 42 results found.
| Name | Description | Type | Package | Framework |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |