BranchAndBound

This class is an optimizer factory which creates a new branch-and-bound algorithm. It can be used to create customized branch-and-bound algorithms with a large degree of freedom.

See also

If you are not familiar with optimizers and optimizer factories, please refer to this page.

Example

Here, we create a simple branch-and-bound algorithm where branching is done on integer variables which are being relaxed. Each node is solved by the external solver GLPK. Nodes are selected according to the “best-bound” rule while variables are selected according to the “most-infeasible” branching rule.

The created algorithm also incorporates sub-tree exploration of maximum depth 2.

model.use(
    BranchAndBound()
        .with_node_optimizer( GLPK::ContinuousRelaxation() )
        .with_branching_rule( MostInfeasible() )
        .with_node_selection_rule( BestBound() )
        .with_subtree_depth(2)
);

Doxygen

Warning

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