This section documents the Gurobi C interface. This manual begins with a quick overview of the functions in the interface, and continues with detailed descriptions of all of the available interface routines.
The first step in using the Gurobi C optimizer is to create an environment, using the GRBloadenv call. The environment acts as a container for all data associated with a set of optimization runs. You will generally only need one environment in your program, even if you wish to work with multiple optimization models. Once you are done with an environment, you should call GRBfreeenv to release the associated resources.
For more advanced use cases, you can use the GRBemptyenv routine to create an uninitialized environment and then, programmatically, set all required options for your specific requirements. For further details see the Empty Environment section.
You can create one or more optimization models within an environment. A model consists of a set of variables, a linear, quadratic, or piecewise-linear objective function on those variables, and a set of constraints. Each variable has an associated lower bound, upper bound, type (continuous, binary, integer, semi-continuous, or semi-integer), and linear objective coefficient. Each linear constraint has an associated sense (less-than-or-equal, greater-than-or-equal, or equal), and right-hand side value. Refer to this section for more information on variables and constraints.
An optimization model may be specified all at once, through the GRBloadmodel routine, or built incrementally, by first calling GRBnewmodel and then calling GRBaddvars to add variables and GRBaddconstr, GRBaddqconstr, GRBaddsos, or any of the GRBaddgenconstrXxx methods to add constraints. Models are dynamic entities; you can always add or delete variables or constraints.
Specific variables and constraints are referred to throughout the Gurobi C interface using their indices. Variable indices are assigned as variables are added to the model, in a contiguous fashion. The same is true for constraints. In adherence to C language conventions, indices all start at 0.
We often refer to the class of an optimization model. A model with a linear objective function, linear constraints, and continuous variables is a Linear Program (LP). If the objective is quadratic, the model is a Quadratic Program (QP). If any of the constraints are quadratic, the model is a Quadratically-Constrained Program (QCP). We will sometimes refer to a few special cases of QCP: QCPs with convex constraints, QCPs with non-convex constraints, bilinear programs, and Second-Order Cone Programs (SOCP). If the model contains any integer variables, semi-continuous variables, semi-integer variables, Special Ordered Set (SOS) constraints, or general constraints, the model is a Mixed Integer Program (MIP). We'll also sometimes discuss special cases of MIP, including Mixed Integer Linear Programs (MILP), Mixed Integer Quadratic Programs (MIQP), Mixed Integer Quadratically-Constrained Programs (MIQCP), and Mixed Integer Second-Order Cone Programs (MISOCP). The Gurobi Optimizer handles all of these model classes.
Solving a Model
Once you have built a model, you can call
GRBoptimize to compute a solution.
GRBoptimize() will use the
to solve LP models, the barrier algorithm to solve QP models
with convex objectives and
QCP models with convex constraints,
and the branch-and-cut algorithm otherwise.
The solution is stored as a set of
attributes of the model. The C interface contains an extensive
set of routines for querying these attributes.
The Gurobi algorithms keep careful track of the state of the model,
so calls to
GRBoptimize() will only perform further
optimization if relevant data has changed since the model was last
optimized. If you would like to discard previously computed solution
information and restart the optimization from scratch without changing
the model, you can call
After a MIP model has been solved, you can call GRBfixmodel to compute the associated fixed model. This model is identical to the input model, except that all integer variables are fixed to their values in the MIP solution. In some applications, it is useful to compute information on this continuous version of the MIP model (e.g., dual variables, sensitivity information, etc.).
Multiple Solutions, Objectives, and Scenarios
By default, the Gurobi Optimizer assumes that your goal is to find one proven optimal solution to a single model with a single objective function. Gurobi provides the following features that allow you to relax these assumptions:
- Solution Pool: Allows you to find more solutions.
- Multiple Scenarios: Allows you to find solutions to multiple, related models.
- Multiple Objectives: Allows you to specify multiple objective functions and control the trade-off between them.
You have a few options if a model is found to be infeasible. You can try to diagnose the cause of the infeasibility, attempt to repair the infeasibility, or both. To obtain information that can be useful for diagnosing the cause of an infeasibility, call GRBcomputeIIS to compute an Irreducible Inconsistent Subsystem (IIS). This routine can be used for both continuous and MIP models, but you should be aware that the MIP version can be quite expensive. This routine populates a set of IIS attributes.
To attempt to repair an infeasibility, call GRBfeasrelax to compute a feasibility relaxation for the model. This relaxation allows you to find a solution that minimizes the magnitude of the constraint violation.
Querying and Modifying Attributes
Most of the information associated with a Gurobi model is stored in a
set of attributes. Some attributes are associated with the variables
of the model, some with the constraints of the model, and some with
the model itself. To give a simple example, solving an optimization
model causes the
X variable attribute to be populated.
Attributes such as
X that are computed by the Gurobi optimizer
cannot be modified directly by the user, while others, such as the
variable lower bound array (the
LB attribute) can.
The Gurobi C interface contains an extensive set of routines for querying or modifying attribute values. The exact routine to use for a particular attribute depends on the type of the attribute. As mentioned earlier, attributes can be either variable attributes, constraint attributes, or model attributes. Variable and constraint attributes are arrays, and use a set of array attribute routines. Model attributes are scalars, and use a set of scalar routines. Attribute values can additionally be of type char, int, double, or string (really char *).
Scalar model attributes are accessed through a set of
GRBget*attr() routines (e.g.,
GRBgetintattr). In addition,
those model attributes that can be set directly by the user (e.g., the
objective sense) may be modified through the
Array attributes are accessed through three sets of routines. The
first set, the
GRBget*attrarray() routines (e.g.,
return a contiguous sub-array of the attribute array, specified using
the index of the first member and the length of the desired sub-array.
The second set, the
GRBget*attrelement() routines (e.g.,
return a single entry from the attribute array. Finally, the
GRBget*attrlist() routines (e.g.,
attribute values for a list of indices.
Array attributes that can be set by the user are modified through the
The full list of Gurobi attributes can be found in the Attributes section.
Additional Model Modification Information
Most modifications to an existing model are done through the attribute interface (e.g., changes to variable bounds, constraint right-hand sides, etc.). The main exceptions are modifications to the constraints themselves, and to the quadratic and piecewise-linear portions of the objective function.
The constraint matrix can be modified in a few ways. The first is to call GRBchgcoeffs to change individual matrix coefficients. This routine can be used to modify the value of an existing non-zero, to set an existing non-zero to zero, or to create a new non-zero. The constraint matrix is also modified when you remove constraints (through GRBdelconstrs) or variables (through GRBdelvars). The non-zero values associated with the deleted constraints or variables are removed along with the constraints or variables themselves.
Quadratic objective terms are added to the objective function using the
GRBaddqpterms routine. You can
add a list of quadratic terms in one call, or you can add terms
incrementally through multiple calls. The
GRBdelq routine allows you to delete
all quadratic terms from the model. Note that quadratic models will
typically have both quadratic and linear terms. Linear terms are
entered and modified through the
Obj attribute, in the same
way that they are handled for models with purely linear objective
If your variables have piecewise-linear objectives, you can specify
them using the GRBsetpwlobj
routine. Call this routine once for each relevant variable. The
Gurobi simplex solver includes algorithmic support for convex
piecewise-linear objective functions, so for continuous models you
should see a substantial performance benefit from using this feature.
To clear a previously specified piecewise-linear objective function,
simply set the
Obj attribute on the corresponding variable to
One important item to note about model modification in the Gurobi optimizer is that it is performed in a lazy fashion, meaning that modifications don't affect the model immediately. Rather, they are queued and applied later. If your program simply creates a model and solves it, you will probably never notice this behavior. However, if you ask for information about the model before your modifications have been applied, the details of the lazy update approach may be relevant to you.
As we just noted, model modifications (bound changes, right-hand side changes, objective changes, etc.) are placed in a queue. These queued modifications can be applied to the model in three different ways. The first is by an explicit call to GRBupdatemodel. The second is by a call to GRBoptimize. The third is by a call to GRBwrite to write out the model. The first case gives you fine-grained control over when modifications are applied. The second and third make the assumption that you want all pending modifications to be applied before you optimize your model or write it to disk.
Why does the Gurobi interface behave in this manner? There are a few reasons. The first is that this approach makes it much easier to perform multiple modifications to a model, since the model remains unchanged between modifications. The second is that processing model modifications can be expensive, particularly in a Compute Server environment, where modifications require communication between machines. Thus, it is useful to have visibility into exactly when these modifications are applied. In general, if your program needs to make multiple modifications to the model, you should aim to make them in phases, where you make a set of modifications, then update, then make more modifications, then update again, etc. Updating after each individual modification can be extremely expensive.
If you forget to call update, your program won't crash. Your query will simply return the value of the requested data from the point of the last update. If the object you tried to query didn't exist then, you'll get an INDEX_OUT_OF_RANGE error instead.
The semantics of lazy updates have changed since earlier Gurobi versions. While the vast majority of programs are unaffected by this change, you can use the UpdateMode parameter to revert to the earlier behavior if you run into an issue.
The Gurobi optimizer provides a set of parameters that allow you to
control many of the details of the optimization process. Factors like
feasibility and optimality tolerances, choices of algorithms,
strategies for exploring the MIP search tree, etc., can be controlled
by modifying Gurobi parameters before beginning the optimization.
Parameters are set using the
GRBset*param() routines (e.g.,
values can be retrieved with the
Parameters can be of type int, double, or char
* (string). You can also read a set of parameter settings from a
file using GRBreadparams, or
write the set of changed parameters using
We also include an automated parameter tuning tool that explores many different sets of parameter changes in order to find a set that improves performance. You can call GRBtunemodel to invoke the tuning tool on a model. Refer to the parameter tuning tool section for more information.
One thing we should note is that each model gets its own copy of the environment when it is created. Parameter changes to the original environment therefore have no effect on existing models. Use GRBgetenv to retrieve the environment associated with a particular model if you want to change a parameter for that model.
Monitoring Progress - Logging and Callbacks
Progress of the optimization can be monitored through Gurobi logging. By default, Gurobi will send output to the screen. A few simple controls are available for modifying the default logging behavior. If you would like to direct output to a file as well as to the screen, specify the log file name in GRBloadenv when you create your environment. You can modify the LogFile parameter if you wish to redirect the log to a different file after creating the environment. The frequency of logging output can be controlled with the DisplayInterval parameter, and logging can be turned off entirely with the OutputFlag parameter. A detailed description of the Gurobi log file can be found in the Logging section.
More detailed progress monitoring can be done through the Gurobi callback function. The GRBsetcallbackfunc routine allows you to install a function that the Gurobi optimizer will call regularly during the optimization process. You can call GRBcbget from within the callback to obtain additional information about the state of the optimization.
Modifying Solver Behavior - Callbacks
Callbacks can also be used to modify the behavior of the Gurobi optimizer. If you call routine GRBterminate from within a callback, for example, the optimizer will terminate at the earliest convenient point. Routine GRBcbsolution allows you to inject a feasible solution (or partial solution) during the solution of a MIP model. Routines GRBcbcut and GRBcblazy allow you to add cutting planes and lazy constraints during a MIP optimization, respectively. Routine GRBcbstoponemultiobj allows you to interrupt the optimization process of one of the optimization steps in a multi-objective MIP problem without stopping the hierarchical optimization process.
Gurobi Compute Server enables programs to offload optimization computations onto dedicated servers. The Gurobi Cluster Manager adds a number of additional capabilities on top of this. One important one, batch optimization, allows you to build an optimization model with your client program, submit it to a Compute Server cluster (through the Cluster Manager), and later check on the status of the model and retrieve its solution. You can use a Batch object to make it easier to work with batches. For details on batches, please refer to the Batch Optimization section.
Most of the Gurobi C library routines return an integer error code. A zero return value indicates that the routine completed successfully, while a non-zero value indicates that an error occurred. The list of possible error return codes can be found in the Error Codes section.
When an error occurs, additional information on the error can be obtained by calling GRBgeterrormsg.