Initializing help system before first use

# problem.strongbranch

Purpose
This subroutine is deprecated and will be removed in a future release. Use problem.strongBranch instead.
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 Deprecated functionality

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