Description The lpSolveAPI package provides an R interface to ‘lp_solve’, .. Please see the link in the references for a discussion of special ordered set (SOS ). lpSolve: Interface to ‘Lp_solve’ v. to Solve CRAN checks: lpSolve results. Downloads: Reference manual: Package source. Matrices can directly be transferred between Scilab and lpsolve in both directions . Some are exactly as described in the reference guide, others have a slightly.
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lp_solve reference guide ()
It uses a different input format and solver options from the LP call and is the preferred method for solving linear programming problems. A missing value is treated as 0.
For a range constraint, b is its constraint upper bound. The default value is used if an option is not specified or its value is a missing value.
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The default value is 1. A value of 0 prints warning lpsolvd error messages only, whereas 1 prints solution information in addition to warning and error messages. The default value is 0. The default value is effectively unbounded.
The default value is 2. The default value is. The values can be E, L, G, or R for equal, less than or equal to, greater than or equal to, giude range constraint.
If this vector is missing, the solver treats the constraints as E type constraints. The row sense for a range constraint is R.
For the non-range constraints, the corresponding values are ignored. For a range constraint, the range value is the difference between its constraint lower bound and its constraint upper bound bso it must be nonnegative.
If you do not specify l or l[j] has a missing value, then the lower bound of variable j is assumed to be 0. If you do not specify u or u[j] has a missing referehce, the opsolve bound of variable j is assumed to be infinity.
A standard linear program has the following formulation:. The primal and dual simplex solvers implement the two-phase simplex method. In phase I, the solver tries to find a feasible solution. If it does not find a feasible solution the LP is infeasible; otherwise, the solver enters phase II to solve the original LP. The interior point solver implements a primal-dual predictor-corrector interior point algorithm.
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The maximum number of iterations was exceeded. The solution is unbounded or infeasible. The subroutine could not obtain enough memory. The subroutine failed to solve the problem.