MOSEK also provides a more flexible

MOSEK also provides a more flexible method of concurrent optimization by using the function
mosek.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 mosek.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 mosek.Task.optimizeconcurrent function is included
below. The example solves a single problem using the interior-point and primal simplex optimizers in
parallel.

ผลลัพธ์ (อังกฤษ) 2:

MOSEK also provides a more Flexible method of concurrent Optimization by using the function
Mosek.Task.optimizeconcurrent. Main advantages of this are that the function 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. May this 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 Mosek.iparam.sim Primal selection. parameter)
is the Best. Can You Solve the Problem in this Case with the Primal simplex Optimizer using several
different selection Concurrently Strategies.
An example Demonstrating the usage of the function is included Mosek.Task.optimizeconcurrent
Below. Problem using the example solves a single and the Interior-Point Primal simplex Optimizers in
parallel.