Model not feasible while solution does exists

Answered

Huib Meulenbelt

November 11, 2022 12:42

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Hi,

I create model as follows

def create_model() -> gurobi.Model:
    m = gurobi.Model()
    m.Params.LogToConsole = 1
    m.Params.PoolSearchMode = 1
    m.Params.PoolSolutions = 1
    m.Params.PoolGap = 0
    m.Params.NonConvex = 2

    return m

Next, 

I introduce a number of variables and constraints:

proj_matrix = [[-0.47686108766245655, 0.8789748939150358, -0.002576614140793262], 
               [-0.8789639728247254, -0.4768680170660497, -0.00438506278966113], 
               [-0.005083064976417589, 0.00017368519028203956, 0.9999870660583067]]

shot_x = m.addVar(vtype=gurobi.GRB.CONTINUOUS, name='shot_x')
shot_y = m.addVar(vtype=gurobi.GRB.CONTINUOUS, name='shot_y')
shot_z = m.addVar(vtype=gurobi.GRB.CONTINUOUS, name='shot_z')

condition_x = shot_x * proj_matrix[0][0] + shot_y * proj_matrix[1][0] + shot_z * proj_matrix[2][0]
condition_y = shot_x * proj_matrix[0][1] + shot_y * proj_matrix[1][1] + shot_z * proj_matrix[2][1]
condition_z = shot_x * proj_matrix[0][2] + shot_y * proj_matrix[1][2] + shot_z * proj_matrix[2][2]

proj_z = -24.008363727141393
proj_x = -0.2297853284073595
proj_y = -35.20627970757762

m.addConstr(gurobi.quicksum([condition_x]) == proj_x)
m.addConstr(gurobi.quicksum([condition_y]) == proj_y)
m.addConstr(gurobi.quicksum([condition_z]) == proj_z)

m.update()
m.optimize()
print(f'number of solutions: {m.SolCount}')

The above results in no solution. However. I know that the correct solution is:

x = -30.77400000000125
y = 17.09599999999955 
z = -24.013000000000005

What am I doing wrong? Thanks in advance!

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• Official comment

  

  Simranjit Kaur

  • Gurobi Staff

  November 16, 2025 23:25

  This post is more than three years old. Some information may not be up to date. For current information, please check the Gurobi Documentation or Knowledge Base. If you need more help, please create a new post in the community forum, or try Gurobot, our chatbot interface offering instant, expert-level support.
• 

  Jaromił Najman

  • Gurobi Staff

  November 11, 2022 13:18

  The default lower bound for variables is \(0\). You have to explicitly state that a variable is allowed to attain negative values.

  shot_x = m.addVar(lb=-gurobi.GRB.INFINITY, vtype=gurobi.GRB.CONTINUOUS, name='shot_x')
  shot_y = m.addVar(lb=-gurobi.GRB.INFINITY, vtype=gurobi.GRB.CONTINUOUS, name='shot_y')
  shot_z = m.addVar(lb=-gurobi.GRB.INFINITY, vtype=gurobi.GRB.CONTINUOUS, name='shot_z')

  Best regards, 
  Jaromił

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• 

  David Torres Sanchez

  • Gurobi Staff

  November 11, 2022 13:28

  Hi,
  
  The default lower bound of variables is 0.0  Model.addVar().
  You can change this when you create the variables:

  shot_x = m.addVar(lb=-100, vtype=gurobi.GRB.CONTINUOUS, name='shot_x')

  Additionally, you don't need to add the \(\texttt{quicksum}\) expression as your \(\texttt{condition_*}\) are already LinExpr.
  
  Cheers, 
  David

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• 

  Huib Meulenbelt

  November 11, 2022 13:40

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  Thank you Jaromil and David!

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