Cplex AdvInd (Advanced Start Switch) equivalent in Gurobi

Answered

Rahul Patel

• February 16, 2021 05:41

Hello,

In CPLEX there is an AdvInd flag that can: 
"For MIP models, setting 1 (one) will cause CPLEX to continue with a partially explored MIP tree if one is available. If tree exploration has not yet begun, setting 1 (one) specifies that CPLEX should use a loaded MIP start, if available. Setting 2 retains the current incumbent (if there is one), re-applies presolve, and starts a new search from a new root."
https://www.ibm.com/support/knowledgecenter/SSSA5P_20.1.0/ilog.odms.cplex.help/CPLEX/Parameters/topics/AdvInd.html

By default that parameter is set to one. However, I am not able to find a Gurobi equivalent for the same. I have a Gurobi model in which I solve two very similar optimization problems, per iteration, with some change in bounds and objective function. Gurobi seems to start afresh while solving the second optimization problem; the C++ + Cplex implementation is 5-10x faster than Python + Gurobi. Is this because of the inherent speed bottleneck of Python?

The task is to solve a multiobjective optimization problem. The model is defined as follows:

def _initialize_base_model(self):
    self.m = gp.Model('moo_knapsack')
    self.v_items = self.m.addVars(self.n_variables, vtype=GRB.BINARY, name='item')
    self.v_objs = self.m.addVars(self.n_objectives,
                                 lb=-GRB.INFINITY,
                                 vtype=GRB.CONTINUOUS,
                                 name="obj")
    self.c_capacity = self.m.addConstr(gp.quicksum(self.weight[i] * self.v_items[i]
                                                   for i in range(self.n_variables))
                                       <= self.capacity,
                                       name='capacity')
    for j in range(self.n_objectives):
        self.m.addConstr(
            self.v_objs[j] == gp.quicksum(self.objectives[j, i] * self.v_items[i]
                                          for i in range(self.n_variables)))
    self.m.setParam(GRB.Param.TimeLimit, self.cfg['time_limit'])
    self.m.update()

And the two related optimization problems we solve are as follows:

def get_solution(self, epsilon=None):
    """Get a (efficient, nondominated) solution tuple for a
    given epsilon"""
    x, z = None, None

    # Solve P
    self.m.setObjective(self.v_objs[self.K])
    j = 0
    for i in range(self.n_objectives):
        self.v_objs[i].ub = epsilon[j] - self.delta if i != self.K else self.ub[self.K]
        j = j + 1 if i != self.K else j
    self.m.optimize()
    if self.m.getAttr('Status') == OPTIMAL:
        z_p = np.floor(self.m.ObjVal + 0.5)
        # Solve Q
        self.m.setObjective(gp.quicksum(self.v_objs))
        self.v_objs[self.K].ub = z_p
        self.m.optimize()
        if self.m.getAttr('Status') == OPTIMAL:
            x = [np.floor(self.v_items[i].x + 0.5) for i in range(self.n_variables)]
            z = np.matmul(self.objectives, x)

    return x, z

Any help will be much appreciated. 

Thank you.

0

• 

  Jaromił Najman

  Gurobi Staff

  • March 01, 2021 12:37

  Hi Rahul,

  A feature similar to the CPLEX AdvInd setting is on our future roadmap and will be added in an upcoming release.

  For now, could you share a Gurobi LOG file of the two subsequent runs performed one after each other, where you think that no information is being re-used? Maybe, we can find a different workaround.

  Best regards,
  Jaromił

  0
• 

  Masoud Kavoosi

  • April 18, 2022 20:17

  

  

  

  Hi Jaromił,

  Have you added this feature yet?

  Thanks

  Masoud

  0
• 

  Jaromił Najman

  Gurobi Staff

  • April 19, 2022 09:58

  Hi Masoud,

  Yes, with version 9.5, we introduced the parameter LPWarmStart to control this feature.

  Best regards, 
  Jaromił

  0
• 

  Masoud Kavoosi

  • April 19, 2022 13:23

  

  

  

  Great, thanks for the reply!

  Masoud

  0

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