MIP Start Algorithm

Type : Selection

Range : The settings listed below

Default : Automatic

The value of this option determines which LP algorithm should be used to solve the initial relaxation of the MIP. Possible values are:

  • Automatic

  • Primal simplex

  • Dual simplex

  • Network simplex

  • Barrier

  • Sifting

  • Concurrent

The default ‘Automatic’ setting of this option currently selects the concurrent optimizer for root relaxations of mixed integer linear programming models (MILP) and selects the dual simplex optimizer for root relaxations of mixed integer quadratic programming models (MIQP).

For MILP (integer constraints and otherwise continuous variables), all settings are permitted.

For MIQP (integer constraints and positive semi-definite quadratic terms in the objective), settings ‘Sifting’ and ‘Concurrent’ are not implemented; if you happen to choose them, setting ‘Sifting’ reverts to ‘Automatic’ and setting ‘Concurrent’ reverts to ‘Barrier’.

For MIQCP (integer constraints and positive semi-definite quadratic terms among the constraints), only the setting ‘Automatic’ is permitted.

Sifting solves a sequence of LP subproblems, where the results from one subproblem are used to select columns from the original model for inclusion in the next subproblem. This iterative sifting process eventually converges to an optimal solution for the original model. Sifting is especially applicable to models with many more columns than rows.

On a multiprocessor computer, the concurrent optimizer launches distinct LP optimizers on multiple threads to solve the initial relaxation of the MIP, terminating as soon as the first optimizer finishes. The concurrent optimizer is described in the section Parallel Concurrent Optimizer . The amount of threads available to the concurrent optimizer is controlled by the option Global Thread Limit .

The concurrent optimizer requires more memory than any individual optimizer, and of course it adds system load by consuming more aggregate CPU time than the fastest individual optimizer would alone. However, the advantages offered in terms of robust solution of models, and assurance in most cases of the minimum solution time, will make it attractive in many situations.

Note

  • The option MIP Method determines which continuous optimizer will be used to solve the subproblems in a MIP, after the initial relaxation.

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