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problem.strongBranchCB

Purpose
Performs strong branching iterations on all specified bound changes. For each candidate bound change, problem.strongBranchCB performs dual simplex iterations starting from the current optimal solution of the base LP, and returns both the status and objective value reached after these iterations.
Topic areas
Branching, Optimizer
Synopsis
objval, status = problem.strongBranchCB(colind, bndtype, bndval, iterlim, callback, data)
strongbranchsolve(cbprob, cbdata, bndidx)
Arguments
colind: list[var] 
Integer array containing the indices of the columns on which the bounds will change.
bndtype: list[str] 
Character array indicating the type of bound to change:
U 
indicates change the upper bound;
L 
indicates change the lower bound;
B 
indicates change both bounds, i.e. fix the column.
bndval: list[float] 
Double array giving the new bound values.
iterlim: int 
Maximum number of LP iterations to perform for each bound change.
callback 
Function to be called after each strong branch has been reoptimized. This function returns an integer. Use 0 to indicate that everything went fine. Use a return value different from 0 to signal an error. This will terminate the function.
data: Any 
User context to be provided for callback.
cbprob: problem 
The problem passed to the callback function, callback.
cbdata: Any 
The user-defined data passed as data.
bndidx: int 
The index of the bound for which callback is called.
Return value
objval: list[float] 
Array of objective values of each LP after performing the strong branching iterations.
status: list[int] 
Array of statuses of each LP after performing the strong branching iterations, as detailed for the LPSTATUS attribute.
Further information
1. This function currently does not apply to general nonlinear problems.
2. Prior to calling problem.strongBranchCB, the current LP problem must have been solved to optimality and an optimal basis must be available.
3. problem.strongBranchCB is an extension to problem.strongBranch. If identical input arguments are provided both will return identical results, the difference being that for the case of problem.strongBranchCB the callback function is called at the end of each LP reoptimization.
4. For each branch optimized, the LP can be interrogated: the LP status of the branch is available through checking LPSTATUS, and the objective function value is available through LPOBJVAL. It is possible to access the full current LP solution by using problem.getCallbackSolution.
5. The return value of callback function callback is currently ignored.
6. Argument colind may contain either xpress.var objects or integer indices.

Parent Topic Xpress problem object methods

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