Source: lp-solve Section: math Priority: optional Maintainer: Juan Esteban Monsalve Tobon Uploaders: Rene Engelhard , Anibal Monsalve Salazar Build-Depends: debhelper (>= 9), libsuitesparse-dev (>= 1:3.4.0) Standards-Version: 4.5.0 Homepage: http://lpsolve.sourceforge.net Package: lp-solve Architecture: any Depends: ${shlibs:Depends}, ${misc:Depends} Description: Solve (mixed integer) linear programming problems The linear programming (LP) problem can be formulated as: Solve A.x >= V1, with V2.x maximal. A is a matrix, x is a vector of (nonnegative) variables, V1 is a vector called the right hand side, and V2 is a vector specifying the objective function. . An integer linear programming (ILP) problem is an LP with the constraint that all the variables are integers. In a mixed integer linear programming (MILP) problem, some of the variables are integer and others are real. . The program lp_solve solves LP, ILP, and MILP problems. It is slightly more general than suggested above, in that every row of A (specifying one constraint) can have its own (in)equality, <=, >= or =. The result specifies values for all variables. . lp_solve uses the 'Simplex' algorithm and sparse matrix methods for pure LP problems. If one or more of the variables is declared integer, the Simplex algorithm is iterated with a branch and bound algorithm, until the desired optimal solution is found. lp_solve can read MPS format input files. Package: lp-solve-doc Section: doc Architecture: all Depends: ${misc:Depends} Recommends: www-browser Description: Solve (mixed integer) linear programming problems - documentation The linear programming (LP) problem can be formulated as: Solve A.x >= V1, with V2.x maximal. A is a matrix, x is a vector of (nonnegative) variables, V1 is a vector called the right hand side, and V2 is a vector specifying the objective function. . An integer linear programming (ILP) problem is an LP with the constraint that all the variables are integers. In a mixed integer linear programming (MILP) problem, some of the variables are integer and others are real. . The program lp_solve solves LP, ILP, and MILP problems. It is slightly more general than suggested above, in that every row of A (specifying one constraint) can have its own (in)equality, <=, >= or =. The result specifies values for all variables. . lp_solve uses the 'Simplex' algorithm and sparse matrix methods for pure LP problems. If one or more of the variables is declared integer, the Simplex algorithm is iterated with a branch and bound algorithm, until the desired optimal solution is found. lp_solve can read MPS format input files. . This package contains the documentation for the lp_solve program and the library. Package: liblpsolve55-dev Section: libdevel Architecture: any Depends: libsuitesparse-dev, ${misc:Depends} Description: Solve (mixed integer) linear programming problems - library The linear programming (LP) problem can be formulated as: Solve A.x >= V1, with V2.x maximal. A is a matrix, x is a vector of (nonnegative) variables, V1 is a vector called the right hand side, and V2 is a vector specifying the objective function. . An integer linear programming (ILP) problem is an LP with the constraint that all the variables are integers. In a mixed integer linear programming (MILP) problem, some of the variables are integer and others are real. . The program lp_solve solves LP, ILP, and MILP problems. It is slightly more general than suggested above, in that every row of A (specifying one constraint) can have its own (in)equality, <=, >= or =. The result specifies values for all variables. . lp_solve uses the 'Simplex' algorithm and sparse matrix methods for pure LP problems. If one or more of the variables is declared integer, the Simplex algorithm is iterated with a branch and bound algorithm, until the desired optimal solution is found. lp_solve can read MPS format input files. . This package contains the static library for developing programs using liblpsolve.