|gurobi||( model )|
|gurobi||( model, params )|
|gurobi||( model, params, env )|
This function optimizes the given model. The algorithm used for the optimization depends on the model type (simplex or barrier for a continuous model; branch-and-cut for a MIP model). Upon successful completion it will return a struct variable containing solution information.
Please consult this section for a discussion of some of the practical issues associated with solving a precisely defined mathematical model using finite-precision floating-point arithmetic.
model: The model struct must contain a valid Gurobi model. See the model argument section for more information.
params: The params struct, when provided, contains a list of modified Gurobi parameters. See the params argument section for more information.
env: The env struct, when provided, allows you to use Gurobi Compute Server or Gurobi Instant Cloud. See the env argument section for more information.
result = gurobi(model, params); if strcmp(result.status, 'OPTIMAL'); fprintf('Optimal objective: %e\n', result.objval); disp(result.x) else fprintf('Optimization returned status: %s\n', result.status); endReturn value:
The optimization result
The gurobi function returns a struct, with the various results of the optimization stored in its fields. The specific results that are available depend on the type of model that was solved, the parameters used, and the status of the optimization. The following is a list of fields that might be available in the returned result. We will discuss the circumstances under which each will be available after presenting the list.
- The status of the optimization, returned as a string.
The desired result is 'OPTIMAL', which indicates that an
optimal solution to the model was found. Other status are possible,
for example if the model has no feasible solution or if you set a
Gurobi parameter that leads to early solver termination. See the
section for further
information on the Gurobi status codes.
- The objective value of the computed solution. Note that
for multi-objective models result.objval will
be a vector, where result.objval(i)
stores the value for model.multiobj(i).
- Best available bound on solution (lower bound for
minimization, upper bound for maximization).
- The best unrounded bound on the optimal objective.
In contrast to objbound, this attribute
does not take advantage of objective integrality information to round
to a tighter bound. For example, if the objective is known to take an
integral value and the current best bound is 1.5, ObjBound will
return 2.0 while ObjBoundC will return 1.5.
- Current relative MIP optimality gap; computed as
and are the MIP
objective bound and incumbent solution objective, respectively).
Returns GRB_INFINITY when an incumbent solution has not yet
been found, when no objective bound is available, or when the current
incumbent objective is 0. This is only available for mixed-integer
- The elapsed wall-clock time (in seconds) for the
- Number of simplex iterations performed.
- Number of barrier iterations performed.
- Number of branch-and-cut nodes explored.
- Magnitude of infeasibility violation in Farkas infeasibility proof. Only available if the model was found to be infeasible. Please refer to FarkasProof for details.
- The computed solution. This vector contains one entry for each
column of A.
- Variable reduced costs for the computed solution. This
vector contains one entry for each column of A.
- Variable basis status values for the computed optimal
basis. You generally should not concern yourself with the contents of
this vector. If you wish to use an advanced start later, you would
simply copy the vbasis and cbasis fields into
the corresponding fields for the next model. This vector contains one
entry for each column of A.
- Unbounded ray. Provides a vector that, when added to any feasible solution, yields a new solution that is also feasible but improves the objective. Only available if the model is found to be unbounded. This vector contains one entry for each column of A.
Linear constraint fields:
- The constraint slack for the computed solution. This
vector contains one entry for each row of A.
- Dual values for the computed solution (also known as shadow prices). This vector contains one entry for each row of
- Constraint basis status values for the computed optimal
basis. This vector contains one entry for each row of A.
- Farkas infeasibility proof. Only available if the model was found to be infeasible. Please refer to FarkasDual for details.
Quadratic constraint fields:
- The quadratic constraint slack in the current solution. This
vector contains one entry for each quadratic constraint.
- The dual values associated with the quadratic constraints. This vector contains one entry for each quadratic constraint.
Solution Pool fields:
- When multiple solutions are found during the
optimization call, these solutions are returned in this field.
A struct array. When present, each struct has the
- Stores the objective value of the -th solution in
Note that when the model is a multi-objective model, instead of a
result.pool(i).objval(j) stores the value of the -th objective function for the -th solution.
- Stores the -th solution in result.pool(i).xn. This vector contains one entry for each column of A.
- For single-objective MIP optimization problems,
this value gives a bound on the best possible objective of an
undiscovered solution. The difference between this value and
objbound is that the former gives an objective bound for
undiscovered solutions, while the latter gives a bound for any
What is Available When
The status field will be present in all cases. It indicates whether Gurobi was able to find a proven optimal solution to the model. In cases where a solution to the model was found, optimal or otherwise, the objval and x fields will be present.
For linear and quadratic programs, if a solution is available, then the pi and rc fields will also be present. For models with quadratic constraints, if the parameter qcpdual is set to 1, the field qcpi will be present. If the final solution is a basic solution (computed by simplex), then vbasis and cbasis will be present. If the model is an unbounded linear program and the InfUnbdInfo parameter is set to 1, the field unbdray will be present. Finally, if the model is an infeasible linear program and the InfUnbdInfo parameter is set to 1, the fields farkasdual and farkasproof will be set.
For mixed integer problems, no dual information (i.e. pi, slack, rc, vbasis, cbasis, qcslack, qcpi, ubdray or farkasdual) is ever available. When multiple solutions are found, the pool and poolobjbound fields will be present. Depending on the status field value, the fields nodecount, objbound, objbundc and mipgap will be available.
For continuous and mixed-integer models, under normal execution, the fields runtime, itercount and baritercount will be available.