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problem.strongbranch

Purpose
Performs strong branching iterations on all specified bound changes. For each candidate bound change, 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.
Synopsis
problem.strongbranch(colind, bndtype, bndval, iterlim, objval, status)
Arguments
colind 
Array containing the columns (i.e. xpress.var objects, indices, or names) 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 
Array giving the new bound values.
iterlim 
Maximum number of LP iterations to perform for each bound change.
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.
Example
Suppose that the current LP relaxation has two integer columns (columns 0 and 1 which are fractionals at 0.3 and 1.5, respectively, and we want to perform strong branching in order to choose which to branch on. This could be done in the following way: 
objval = []
status = []
p.strongbranch([0,0,1,0], ['','','',''], [1,0,2,1],
                1000, objval, status)
Further information

Prior to calling strongbranch, the current LP problem must have been solved to optimality and an optimal basis must be available.



Parent Topic Xpress problem methods

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