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# C API Overview

## Environments

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 Environment section.

## Models

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 GRBaddgenconstr* methods to add constraints. Models are dynamic entities; you can always add or delete variables or constraints. See Build a model for general guidance or mip1_c.c for a specific example.

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. At the highest level, a model can be continuous or discrete, depending on whether the modeling elements present in the model require discrete decisions to be made. Among continuous models…

- 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 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 any of the constraints are non-linear (chosen from among the available general constraints), the model is a Non-Linear Program (NLP).

A model that contains any integer variables, semi-continuous variables, semi-integer variables, Special Ordered Set (SOS) constraints, or general constraints, is discrete, and is referred to as a Mixed Integer Program (MIP). The special cases of MIP, which are the discrete versions of the continuous models types we’ve already described, are…

- Mixed Integer Linear Programs (MILP)

- Mixed Integer Quadratic Programs (MIQP)

- Mixed Integer Quadratically-Constrained Programs (MIQCP)

- Mixed Integer Second-Order Cone Programs (MISOCP)

- Mixed Integer Non-Linear Programs (MINLP)

The Gurobi Optimizer handles all of these model classes. Note that the boundaries between them aren’t as clear as one might like, because we are often able to transform a model from one class to a simpler class.

## Solving a Model

Once you have built a model, you can call `GRBoptimize` to compute a solution. By default, `GRBoptimize` will use the concurrent optimizer 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 `GRBreset`.

After a MIP model has been solved, you can call `GRBfixmodel` to compute the associated fixed model. This model is identical to the original, except that the integer variables are fixed to their values in the MIP solution. If your model contains SOS constraints, some continuous variables that appear in these constraints may be fixed as well. In some applications, it can be useful to compute information on this fixed model (e.g., dual variables, sensitivity information, etc.), although you should be careful in how you interpret this information.

## 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 (refer to example poolsearch_c.c).

- Multiple Scenarios: Allows you to find solutions to multiple, related models (refer to example multiscenario_c.c).

- Multiple Objectives: Allows you to specify multiple objective functions and control the trade-off between them (refer to example multiobj_c.c).

## Infeasible Models

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. You will find more information about this feature in section Relaxing for Feasibility. Examples are discussed in Diagnose and cope with infeasibility.

## 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 `GRBset*attr()` routines (e.g., `GRBsetdblattr`).

Array attributes are accessed through three sets of routines. The first set, the `GRBget*attrarray()` routines (e.g., `GRBgetcharattrarray`) 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., `GRBgetcharattrelement`) return a single entry from the attribute array. Finally, the `GRBget*attrlist()` routines (e.g., `GRBgetdblattrlist`) retrieve attribute values for a list of indices.

Array attributes that can be set by the user are modified through the `GRBset*attrarray()`, `GRBset*attrelement()`, and `GRBset*attrlist()` routines.

The full list of attributes can be found in the Attributes section of this document. Examples of how to query and set attributes can also be found in this 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 functions.

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 0.

Some examples are discussed in Modify a model.

## Lazy Updates

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.

## Managing Parameters

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., `GRBsetintparam`). Current values can be retrieved with the `GRBget*param()` routines (e.g., `GRBgetdblparam`). 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 `GRBwriteparams`. Refer to the example params_c.c which is considered in Change parameters.

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.

Note that a model gets its own copy of the environment when it is created. Changes to the original environment have no effect on the copy, and vice versa. Use `GRBgetenv` to retrieve the environment associated with a model if you would like to change the parameter value 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. Refer to the example callback_c.c which is discussed in Callbacks.

## 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 (refer to the example tsp_c.c). 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.

## Batch Optimization

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.

## Error Handling

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 table.

When an error occurs, additional information on the error can be obtained by calling `GRBgeterrormsg`.

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