GLPK.Optimizer — Type
Optimizer(;
    want_infeasibility_certificates::Bool = true,
    method::MethodEnum = GLPK.SIMPLEX,
)

Create a new Optimizer object.

Arguments

  • want_infeasibility_certificates::Bool: flag to control whether to attempt to generate an infeasibility certificate in the case of primal or dual infeasibility. Defaults to true. You should set this to false if you want GLPK to report primal or dual infeasiblity, but you don't need a certificate.

  • method::MethodEnum: Solution method to use. Default is GLPK.SIMPLEX. Other options are GLPK.EXACT and GLPK.INTERIOR.

GLPK._add_affine_constraint — Method
_add_affine_constraint(
    problem::Union{Optimizer, Ptr{glp_prob}},
    columns::Vector{Cint},
    coefficients::Vector{Float64},
    sense::Cchar,
    rhs::Float64,
)

Helper function to add a row to the problem. Sense must be one of 'E' (ax == b), 'G' (ax >= b), 'L' (ax <= b).

GLPK._certificates_potentially_available — Method
_certificates_potentially_available(model::Optimizer)

Return true if an infeasiblity certificate or an unbounded ray is potentially available (i.e., the model has been solved using either the Simplex or Exact methods).

GLPK._get_infeasibility_ray — Method
_get_infeasibility_ray(model::Optimizer, ray::Vector{Float64})

Compute a Farkas certificate of primal infeasibility (unbounded dual ray) and store in ray. Returns true if successful, and false if a ray cannot be found.

Assumes ray has been initialized to all 0.0s.

GLPK._get_unbounded_ray — Method
_get_unbounded_ray(model::Optimizer, ray::Vector{Float64})::Bool

Compute an unbounded primal ray and store in ray. Returns true if successful, and false if a ray cannot be found.

Assumes the primal has been solved with primal simplex and is proven unbounded. Assumes ray has been initialized to all 0.0s.

GLPK._round_bounds_to_integer — Method
_round_bounds_to_integer(model)

GLPK does not allow integer variables with fractional bounds. Therefore, we round the bounds of binary and integer variables to integer values prior to solving.

Returns a tuple (column, lower, upper) for the bounds that need to be reset.

GLPK._set_parameter — Method
_set_parameter(param_store, key::Symbol, value)::Bool

Set the field name key in a param_store type (that is one of InteriorParam, IntoptParam, or SimplexParam) to value.

Returns a Bool indicating if the parameter was set.

GLPK._validate_constraint_types — Method
_validate_constraint_types(dest::Optimizer, src::MOI.ModelLike)

Throw an error if unsupported constraint or objective types are present in src.

GLPK.offset — Method
offset(x::Vector)

GLPK uses 1-based indexing for its arrays. But since C has 0-based indexing, all 1-based vectors passed to GLPK need to be padded with a "0'th" element that will never be accessed. To avoid doing this padding in Julia, we convert the vector to a reference, and use the optional second argument to ensure the reference points to the "0'th" element of the array. This is safe to do, provided C never accesses x[0].

See the GLPK manual for more details.