The MOSEK Python optimizer API manual Version 7.0 (Revision 141)

296 APPENDIX A. API REFERENCE
Description:
Solves several instances of the same problem in parallel, with unique parameter settings for each
task. The argument task contains the problem to be solved. taskarray is a pointer to an array
of num empty tasks. The task task and the num tasks pointed to by taskarray are solved in
parallel. That is num + 1 threads are started with one optimizer in each. Each of the tasks can
be initialized with different parameters, e.g different selection of solver.
All the concurrently running tasks are stopped when the optimizer successfully terminates for
one of the tasks. After the function returns task contains the solution found by the task that
finished first.
After Task.optimizeconcurrent returns task holds the optimal solution of the task which
finished first. If all the concurrent optimizations finished without providing an optimal solution
the error code from the solution of the task task is returned.
In summary a call to Task.optimizeconcurrent does the following:
• All data except task parameters (iparam, dparam and sparam) in task is copied to each of
the tasks in taskarray. In particular this means that any solution in task is copied to the
other tasks. Call-back functions are not copied.
• The tasks task and the num tasks in taskarray are started in parallel.
• When a task finishes providing an optimal solution (or a certificate of infeasibility) its
solution is copied to task and all other tasks are stopped.
Observe the concurrent optimizer is not deterministic.
For an explained code example see Section 11.6.4.
A.2.169
Task.optimizersummary()
Task.optimizersummary(whichstream)
Prints a short summary with optimizer statistics for last optimization.
Arguments
whichstream :
streamtype
Index of the stream.
Description:
Prints a short summary with optimizer statistics for last optimization.