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 |
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 |
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 |
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 |
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 |
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 |