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

11.6. SOLVING PROBLEMS IN PARALLEL 151
task.readdata(args[0])
task.putintparam(mosek.iparam.optimizer,
mosek.optimizertype.concurrent)
task.putintparam(mosek.iparam.concurrent num optimizers, 2)
task.optimize()
task.solutionsummary(mosek.streamtype.msg)
# call the main function
try:
main (sys.argv[1:])
except mosek.Exception as e:
print ("ERROR: %s" % str(e.code))
if msg is not None:
print ("\t%s" % e.msg)
sys.exit(1)
except:
import traceback
traceback.print exc()
sys.exit(1)
sys.exit(0)
11.6.4 A more flexible concurrent optimizer
MOSEK also provides a more flexible method of concurrent optimization by using the function
Task.optimizeconcurrent. The main advantages of this function are that it allows the calling application
to assign arbitrary values to the parameters of each tasks, and that call-back functions can
be attached to each task. This may be useful in the following situation: Assume that you know the
primal simplex optimizer to be the best optimizer for your problem, but that you do not know which of
the available selection strategies (as defined by the iparam.sim primal selection parameter) is the
best. In this case you can solve the problem with the primal simplex optimizer using several different
selection strategies concurrently.
An example demonstrating the usage of the Task.optimizeconcurrent function is included below.
The example solves a single problem using the interior-point and primal simplex optimizers in parallel.
##
# Copyright: Copyright (c) MOSEK ApS, Denmark. All rights reserved.
#
# File: concurrent2.py
#
# Purpose: To demonstrate a more flexible interface for concurrent optimization.
##
import sys
import mosek
from mosek.array import array
# Since the actual value of Infinity is ignores, we define it solely