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The MOSEK command line tool Version 7.0 (Revision 141)

The MOSEK command line tool. Version 7.0 ... - Documentation

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6.3. THE MIXED-INTEGER CONIC OPTIMIZER 51<br />

<strong>The</strong> mixed-integer conic optimizer is specialized for solving <strong>line</strong>ar and conic optimization problems. It<br />

can also solve pure quadratic and quadratically constrained problems, these problems are automatically<br />

converted to conic problems before being solved. Whereas the mixed-integer optimizer deals with<br />

quadratic and quadratically constrained problems directly.<br />

<strong>The</strong> mixed-integer conic optimizer is run-to-run deterministic. This means that if a problem is solved<br />

twice on the same computer with identical options then the obtained solution will be bit-for-bit identical<br />

for the two runs. However, if a time limit is set then this may not be case since the time taken to solve<br />

a problem is not deterministic. Moreover, the mixed-integer conic optimizer is parallelized i.e. it can<br />

exploit multiple cores during the optimization. Finally, the mixed-integer conic optimizer is a free addon<br />

to the continuous optimizers. However, for some <strong>line</strong>ar problems the mixed-integer optimizer may<br />

outperform the mixed-integer conic optimizer. On the other hand the mixed-integer conic optimizer<br />

is included with continuous optimizers free of charge and usually the fastest for conic problems.<br />

None of the mixed-integer optimizers handles symmetric matrix variables i.e semi-definite optimization<br />

problems.<br />

6.3 <strong>The</strong> mixed-integer conic optimizer<br />

<strong>The</strong> mixed-integer conic optimizer is employed by setting the parameter MSK IPAR OPTIMIZER to<br />

MSK OPTIMIZER MIXED INT CONIC.<br />

<strong>The</strong> mixed-integer conic employs three phases:<br />

Presolve:<br />

In this phase the optimizer tries to reduce the size of the problem using preprocessing techniques.<br />

Moreover, it strengthens the continuous relaxation, if possible.<br />

Heuristic:<br />

Using heuristics the optimizer tries to guess a good feasible solution.<br />

Optimization:<br />

<strong>The</strong> optimal solution is located using a variant of the branch-and-cut method.<br />

6.3.1 Presolve<br />

In the preprocessing stage redundant variables and constraints are removed. <strong>The</strong> presolve stage can<br />

be turned off using the MSK IPAR MIO PRESOLVE USE parameter.<br />

6.3.2 Heuristic<br />

Initially, the integer optimizer tries to guess a good feasible solution using a heuristic.

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