Xpress base functions
|
Declares an output callback function in the global environment, called every time a line of message text is output by any object in the library. This callback function will be called in addition to any output callbacks already added by xpress.addcbmsghandler.
|
|
|
Returns the evaluation of one or more expressions for a given assignment of values to optimization variables.
|
|
|
Returns the full path to the directory of examples of the Xpress Python interface module.
|
|
|
Returns True if the provided feature is available in the current license used by the optimizer, False otherwise.
|
|
|
Releases the Xpress environment, thus freeing up one license. The subsequent creation of a problem automatically triggers a call to xpress.init.
|
|
|
Returns the banner and copyright message.
|
|
|
Returns whether checking validation of all Optimizer function calls is enabled for the current process. Checking validation is enabled by default but can be disabled by xpress.setcheckedmode.
|
|
|
Queries whether the current application is allowed to use the Insight Compute interface.
|
|
|
Returns the number of days left until an evaluation license expires.
|
|
|
Returns the last error encountered during a call to the Xpress global environment.
|
|
|
Returns the error message string describing the last licensing error, if any occurred.
|
|
|
Returns True if Optimizer messages will be written to the Python output stream, False otherwise.
|
|
|
Returns the full Optimizer version number as a string of the form 15.10.03, where 15 is the major release, 10 is the minor release, and 03 is the build number.
|
|
|
Returns the Optimizer version numbers split into major, minor, and build number.
|
|
|
Initializes the Xpress environment prior to creating or reading a problem. Note that it is not necessary to call this function after importing the Xpress module and before creating or solving a problem, since the environment will be automatically initialized when it is needed for the first time. However, you may want to call xpress.init in a with statement, which allows you to: detect initialization errors (which will be raised as an xpress.ModelError); specify the path to your license file; explicitly acquire the Xpress license; automatically release the Xpress license at the end of the with statement.
|
|
|
Returns the full path to the PDF reference manual of the Python interface.
|
|
|
Removes a message callback function previously added by xpress.addcbmsghandler. The specified callback function will no longer be called after it has been removed.
|
|
|
Sets whether to force the same execution path on various CPU architecture extensions, in particular (pre-)AVX and AVX2.
|
|
|
Disable/enable some of the checking validation of function calls function call parameters for calls to the Xpress Optimizer API. This checking is relatively lightweight but disabling it can improve performance in cases where non-intensive Xpress Optimizer functions are called repeatedly in a short space of time. Please note: after disabling checking and validation for function calls, invalid usage of Xpress Optimizer functions may not be detected and may cause the Xpress Optimizer process to behave unexpectedly or crash. It is not recommended to disable function call checking validation during application development.
|
|
|
Set whether the current application is allowed to use the Insight Compute interface.
|
|
|
Sets one of the module's controls to its default values. This affects all problems created after calling setdefaults, not before.
|
|
|
Sets the module's controls to their default values. This affects all problems created after calling setdefaults, not before.
|
|
|
Enables or disables writing Optimizer messages to the Python output stream.
|
|
|
Creates a dictionary or NumPy array of variables. Similar to the creation of a single variable with xpress.var, vars allows for using one or more index sets, specified as sets, lists, range objects, or any iterable object. Specifying a number k as an argument is equivalent to range(k) but can be used to create NumPy multiarrays of variables, and allows for more efficient creation. The result is otherwise a Python dictionary of variables, whose keys are tuple of indices. A collection of variables x that is created with vars can be indexed, for instance, as x[i,j] where i and j are indices in the lists provided.
|
© 2001-2025 Fair Isaac Corporation. All rights reserved. This documentation is the property of Fair Isaac Corporation (“FICO”). Receipt or possession of this documentation does not convey rights to disclose, reproduce, make derivative works, use, or allow others to use it except solely for internal evaluation purposes to determine whether to purchase a license to the software described in this documentation, or as otherwise set forth in a written software license agreement between you and FICO (or a FICO affiliate). Use of this documentation and the software described in it must conform strictly to the foregoing permitted uses, and no other use is permitted.