Julia> objective_bound(model) # HiGHS only implements objective bound for MIPs If the solver supports it, the value of the dual objective can be obtained via dual_objective_value. The best known bound on the optimal objective value can be obtained via objective_bound. The objective value of a solved problem can be obtained via objective_value. You can also use has_values, which returns true if there is a solution that can be queried, and false otherwise. The first means that the solver doesn't have a solution to return, and the second means that the primal solution is a certificate of dual infeasibility (a primal unbounded ray). Other common returns are NO_SOLUTION, and INFEASIBILITY_CERTIFICATE. Use primal_status to return an MOI.ResultStatusCode enum describing the status of the primal solution. "kHighsModelStatusOptimal" Primal solutions Primal solution status Use raw_status to get a solver-specific string explaining why the optimization stopped: julia> raw_status(model) When the dual is infeasible, the primal is unbounded if there exists a feasible primal solution. The MOI.TerminationStatusCode enum describes the full list of statuses that could be returned.Ĭommon return values include OPTIMAL, LOCALLY_SOLVED, INFEASIBLE, DUAL_INFEASIBLE, and TIME_LIMIT.Ī return status of DUAL_INFEASIBLE does not guarantee that the primal is unbounded. Use termination_status to understand why the solver stopped. Julia> solution_summary(model verbose = true) Solution_summary can be used for checking the summary of the optimization solutions. It uses the following model as an example: julia> begin This section of the manual describes how to access a solved solution to a problem.
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