# Here we use the abs operator of the Python interface to create a new # optimization problem. # # (C) Fair Isaac Corp., 1983-2021 # Find the point that minimizes the l-1 norm within a given polytope, # i.e. the sum of the absolute values of the coordinates of a point in # a polytope. import xpress as xp p = xp.problem() # Read data from a problem of MIPLIB 2017 p.read('pk1.mps.gz') # Retrieve all variables of the original problem x = p.getVariable() # Equivalently to general_constraint_abs.py, we want to minimize the # sum of all absolute values of the original variables. We do so by # using the API functions, but first create a set of variables which # will be used in the objective function and that will be used in the # call to addgencons() later abs_x = [xp.var() for v in x] N = len(x) p.addVariable(abs_x) p.addgencons([xp.gencons_abs]*N, abs_x, [i for i in range(N)], x) # Change objective function to the l-1 norm of the variable vector, to # be minimized. p.setObjective (xp.Sum(abs_x)) # Set time limit to 20 seconds p.controls.maxtime = -20 p.optimize() print("solution:", p.getSolution())