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problem.strongBranch

Purpose
Performs strong branching iterations on all specified bound changes. For each candidate bound change, problem.strongBranch 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 area
Branching
Synopsis
objval, status = problem.strongBranch(colind, bndtype, bndval, iterlim)
Arguments
colind 
Integer array containing the indices of the columns on which the bounds will change.
bndtype 
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 
Double array giving the new bound values.
iterlim 
Maximum number of LP iterations to perform for each bound change.
Return value
objval 
Objective value of each LP after performing the strong branching iterations.
status 
Status 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.strongBranch, the current LP problem must have been solved to optimality and an optimal basis must be available.
3. Argument colind may contain xpress.var objects instead of indices.

Parent Topic Methods of the class problem

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