What is the correct syntax of the function branchobj.addrows?

Hi,

Sorry about the mistake in the documentation, I will get that fixed. Your guess about the syntax is correct. Could it be that the variable you are branching on has been eliminated during presolve? You could test this by disabling presolve, by setting "problem.controls.presolve = 0". If that does not fix the problem, could you provide a complete example that shows how this is not working as you would expect?

The [0, len(ind3)] argument to addrows is to allow one side of the branch to enforce several inequalities. This array indicates where the coefficients of each inequality begin, in the rowtype, rhs, colind and rowcoef arrays. The last element in the array is a pointer past the end of the last coefficient - this is a vestige from the C API where the total number of coefficients must be explicitly specified somewhere.

Below is a complete, working example, where you should see from the "Solution at node x" log lines that the second variable has been branched on using the inequalities specified in the callback.

import xpress as xp
 
def optnode(cbprob, data):
  lpsol = []
  cbprob.getlpsol(lpsol)
  print(f'Solution at node {cbprob.attributes.currentnode}: {lpsol}')
 
def chgbranchobject(cbprob, data, orig_branch):
  # Only add our custom branch at the root node
  if cbprob.attributes.currentnode == 1:
    b = xp.branchobj(cbprob)
    b.addbranches(2)
    col = 1   # index of variable 'large'
    coef = 1  # coefficient of 'large' in the inequalities
    b.addrows(0, ['L'], [20], [0, 1], [col], [coef])  # large <= 20
    b.addrows(1, ['G'], [30], [0, 1], [col], [coef])  # large >= 30
    return b
 
# Small example problem, manufacturing chess sets
p = xp.problem()
small = p.addVariable(vartype=xp.integer) # number of small sets to manufacture
large = p.addVariable(vartype=xp.integer) # number or large sets to manufacture
 
p.addConstraint(3 * small + 2 * large <= 400) # limit on available machine time
p.addConstraint(small + 3 * large <= 200)     # limit on available wood
 
p.setObjective(5 * small + 7 * large, sense=xp.maximize)
 
# Disable presolve so that the indices of the variables are preserved
p.controls.presolve = 0
 
# Turn off cutting and heuristics to ensure a tree search takes place
p.controls.cutstrategy = 0
p.controls.heuremphasis = 0
 
p.addcboptnode(optnode)
p.addcbchgbranchobject(chgbranchobject)
p.optimize()
 
print(p.getSolution())