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Controls and Attributes

A control is a parameter that can influence the performance and behavior of the Xpress Optimizer. For example, the MIP gap, the feasibility tolerance, or the type of root LP algorithms are controls that can be set. Controls can both be read from and written to an optimization problem.

An attribute is a feature of an optimization problem, such as the number of rows and columns or the number of quadratic elements of the objective function. They are read-only parameters in that they can only be modified, for example, by functions for adding constraints or variables, or functions for setting and modifying the objective function.

Both controls and attributes are of three types: integer, floating point, or string. The Xpress Python interface allows for setting and retrieving the value of all controls of an optimization problem, as well as getting the value of all of a problem's attributes.

This reference manual does not describe the meaning of controls and attributes in the Xpress Optimizer; for a detailed description of each, please refer to the Optimizer's reference manual.

Following Python's philosophy, one can set and obtain multiple controls/attributes with one function call. In other words, one can set either (i) a single control and its value; or (ii) a Python dictionary coupling a list of control names and their respective value. Similarly, with one function call one can obtain (i) the value of a single attribute or control by specifying it as a parameter; or (ii) a dictionary associating names to values for each of a list of controls or attributes given as an argument. See the examples below for more information.

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