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# Python API Overview

## Global Functions

For convenience when using Gurobi interactively through the Python interpreter, we provide a set of Global Functions. The most important of these functions is probably the `read` function, which allows you to read and solve a model from file. Another useful global function is `disposeDefaultEnv`, which disposes of the default environment. Other global functions allow you to read, modify, or write Gurobi parameters (`readParams`, `setParam`, and `writeParams`).

## Models

Most actions in the Gurobi Python interface are performed by calling methods on Gurobi objects. The most commonly used object is the `Model`. A model consists of a set of decision variables (objects of class `Var` or `MVar`), a linear or quadratic objective function on these variables (specified using `Model.setObjective`), and a set of constraints on these variables (objects of class `Constr`, `MConstr`, `QConstr`, `MQConstr`, `SOS`, `GenConstr`, or `MGenConstr`). Each variable has an associated lower bound, upper bound, and type (continuous, binary, etc.). Each linear or quadratic 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, constraints, and objectives.

An optimization model may be specified all at once, by loading the model from a file (using the previously mentioned `read` function), or it may be built incrementally, by first constructing an empty object of class `Model` and then subsequently calling `Model.addVar`, `Model.addVars`, or `Model.addMVar` to add additional variables, and `Model.addConstr`, `Model.addConstrs`, `Model.addLConstr`, `Model.addQConstr`, `Model.addSOS`, or any of the `Model.addGenConstr*` methods to add additional constraints. See Build a model for general guidance or mip1_remote.py for a specific example.

Linear constraints are specified by building linear expressions (objects of class `LinExpr` or `MLinExpr`), and then specifying relationships between these expressions (for example, requiring that one expression be equal to another). Quadratic constraints are built in a similar fashion, but using quadratic expressions (objects of class `QuadExpr` or `MQuadExpr`) instead. General constraints are built using a set of dedicated methods, or a set of general constraint helper functions plus overloaded operators.

Nonlinear constraints are specified by building nonlinear expressions (objects of class `NLExpr` or `MNLExpr`) and specifying a chosen resultant variable which takes the value of the expression. That resultant variable can then participate in other constraints. In the Python API, nonlinear expressions are built using operator overloading. In general, calling any nonlinear function, or creating any expression which is not strictly linear or quadratic (in terms of the model variables) results in a nonlinear expression.

Models are dynamic entities; you can always add or remove variables or constraints.

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.

## Environments

Environments play a much smaller role in the Gurobi Python interface than they do in our other language APIs, mainly because the Python interface has a default environment. Unless you explicitly pass your own environment to routines that require an environment, the default environment will be used.

The main situation where you may want to create your own environment is when you want precise control over when the resources associated with an environment (specifically, a licensing token or a Compute Server) are released. If you use your own environment to create models (using `read` or the `Model` constructor), then the resources associated with the environment will be released as soon as your program no longer references your environment or any models created with that environment.

Note that you can manually remove the reference to the default environment, thus making it available for garbage collection, by calling `disposeDefaultEnv`. After calling this, and after all models built within the default environment are garbage collected, the default environment will be garbage collected as well. A new default environment will be created automatically if you call a routine that needs one.

For more advanced use cases, you can use an empty environment to create an uninitialized environment and then, programmatically, set all required options for your specific requirements. For further details see the Environment section.

## Solving a Model

Once you have built a model, you can call `Model.optimize` to compute a solution. By default, `optimize` 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 in a set of attributes of the model, which can be subsequently queried (we will return to this topic shortly).

The Gurobi algorithms keep careful track of the state of the model, so calls to `Model.optimize` 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 `Model.reset`.

After a MIP model has been solved, you can call `Model.fixed` 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.py).

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

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

## 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 `Model.computeIIS` to compute an Irreducible Inconsistent Subsystem (IIS). This method can be used for both continuous and MIP models, but you should be aware that the MIP version can be quite expensive. This method populates a set of IIS attributes.

To attempt to repair an infeasibility, call `Model.feasRelaxS` or `Model.feasRelax` 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 (the `lb` attribute) can.

Attributes can be accessed in two ways in the Python interface. The first is to use the `getAttr()` and `setAttr()` methods, which are available on variables (`Var.getAttr`/ `Var.setAttr`), matrix variables (`MVar.getAttr`/ `MVar.setAttr`), linear constraints (`Constr.getAttr`/ `Constr.setAttr`), matrix constraints (`MConstr.getAttr`/ `MConstr.setAttr`), quadratic constraints (`QConstr.getAttr`/ `QConstr.setAttr`), matrix constraints (`MQConstr.getAttr`/ `MQConstr.setAttr`), SOSs (`SOS.getAttr`), general constraints (`GenConstr.getAttr`/ `GenConstr.setAttr`), and models (`Model.getAttr`/ `Model.setAttr`). These are called with the attribute name as the first argument (e.g., `var.getAttr("x")` or `constr.setAttr("rhs", 0.0)`). The full list of available attributes can be found in the Attributes section of this manual.

Attributes can also be accessed more directly: you can follow an object name by a period, followed by the name of an attribute of that object. Note that upper/lower case is ignored when referring to attributes. Thus, `b = constr.rhs` is equivalent to `b = constr.getAttr("rhs")`, and `constr.rhs = 0.0` is equivalent to `constr.setAttr("rhs", 0.0)`.

## 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 constraint matrix and to the objective function.

The constraint matrix can be modified in a few ways. The first is to call the `Model.chgCoeff` method. This method 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 a variable or constraint from the model (through the `Model.remove` method). The non-zero values associated with the deleted constraint or variable are removed along with the constraint or variable itself.

The model objective function can also be modified in a few ways. The easiest is to build an expression that captures the objective function (a `LinExpr`, `MLinExpr`, `QuadExpr`, or `MQuadExpr` object), and then pass that expression to method `Model.setObjective`. If you wish to modify the objective, you can simply call `Model.setObjective` again with a new `LinExpr` or `QuadExpr` object.

For linear objective functions, an alternative to `Model.setObjective` is to use the Obj variable attribute to modify individual linear objective coefficients.

If your variables have piecewise-linear objectives, you can specify them using the `Model.setPWLObj` method. Call this method 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 `Model.update`. The second is by a call to `Model.optimize`. The third is by a call to `Model.write` 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, Gurobi will throw an exception with error code NOT_IN_MODEL.

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 method `Model.setParam`. Current values may also be retrieved with `Model.getParamInfo`. You can also access parameters more directly through the `Model.Params` class. To set the MIPGap parameter to 0.0 for model `m`, for example, you can do either `m.setParam('MIPGap', 0)` or `m.Params.MIPGap = 0`. Refer to the example params.py which is considered in Change parameters.

You can read a set of parameter settings from a file using `Model.read`, or write the set of changed parameters using `Model.write`.

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 `Model.tune` 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 changing a parameter for one model has no effect on the parameter value for other models. Use the global `setParam` method to set a parameter for all loaded models.

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.

## Monitoring Progress - Logging

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. You can set the LogFile parameter if you wish to also direct the Gurobi log to a file. Printing of log messages to the screen (standard output) can be suppressed using the LogToConsole parameter. The frequency of logging output can be controlled with the DisplayInterval parameter, and logging can be turned off entirely with the OutputFlag parameter.

Log output is also sent to a Python logger named `gurobipy`, at level `INFO`. You can use the Python `logging` module to connect to this log. Messages are always sent to the logger, unless OutputFlag is set to 0.

If you want log messages to be sent to the Python logger but not printed to standard output, you should leave OutputFlag at its default value and set LogToConsole to 0. To suppress all Gurobi log messages on standard output, you must set this parameter when starting the environment. The following example demonstrates proper usage, along with a call to `logging.basicConfig` to emit Gurobi log messages through the Python logger:

    import logging
    import gurobipy as gp

    logging.basicConfig(
       level=logging.INFO,
       format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
    )

    env = gp.Env(params={"LogToConsole": 0})
    model = gp.Model(env=env)
    ...
    model.optimize()

## Monitoring Progress - Callbacks

More detailed progress monitoring can be done through a callback function. If you pass a function taking two arguments, `model` and `where`, to `Model.optimize`, your function will be called periodically from within the optimization. Your callback can then call `Model.cbGet` to retrieve additional information on the state of the optimization. You can refer to the Callbacks section for additional information. Refer to the example callback.py which is discussed in Callbacks.

## Modifying Solver Behavior - Callbacks

Callbacks can also be used to modify the behavior of the Gurobi optimizer. The simplest control callback is `Model.terminate`, which asks the optimizer to terminate at the earliest convenient point. Method `Model.cbSetSolution` allows you to inject a feasible solution (or partial solution) during the solution of a MIP model. Methods `Model.cbCut` and `Model.cbLazy` allow you to add cutting planes and lazy constraints during a MIP optimization, respectively (refer to the example tsp.py). Method `Model.cbStopOneMultiObj` 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

All of the methods in the Gurobi Python library can throw an exception of type `GurobiError`. When an exception occurs, additional information on the error can be obtained by retrieving the `errno` or `message` members of the `GurobiError` object. A list of possible values for the `errno` field can be found in the Error Code table.

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