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TypeError: unsupported operand type(s) for +: 'int' and 'GenExpr'

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Aldo -
  • Gurobi-versary
  • First Question

Hi,

I am trying to code this objective function but it returns error.

Below is the objective function:

Below is my code:

I'm not sure how to make the general expression to integer as it the matrix multiplication will return 1x1 matrix. 

Thank you for reading this post and helping me out!

0

Comments

5 comments

Date Votes
  • Official comment
    Simranjit Kaur
    • Gurobi Staff
    Edited
    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 why not try our AI Gurobot?.
  • Maliheh Aramon
    • Gurobi Staff
    Hi Aldo, 
     
    You see this error because you are trying to sum over Gurobi general expressions (GenExpr). The error traces back to the part below:
    sum(max_((beta - (stocks @ ret[i])), 0) for i in range(T))
     
    The GenExpr are temporary objects and do not have any member functions such as addition or subtraction. 
     
    We suggest defining a new decision variable \( y = (\beta - x^T \hat{r}^t)^+\). This allows to define your objective function in terms of \(y\) and then add constraints to enforce the relationship between \(y\) and \(x\). 
     
    Please be aware that you will see an error if having:
    y = model.addMVar(T, name="y")
    model.addConstrs(
         (y[i] == max_((beta - (stocks @ ret[i])), 0) for i in range(T)), name="set_max"
    )
    The main reason is the left and the right hand sides do not have the right types (see below copied from the documentation). General constraint helper functions like max_() are not currently integrated with matrix variable objects.
    • Min or Max Constraint: an expression of the form x == op_(y), where x is a Var object, and y is a Var, a list of Var and constants, or a tupledict of Var, and op_ is one of min_ or max_.

    If you want to continue using GenExpr, you can use MVar.tolist() to map MVar objects to Var objects.

    y = model.addMVar(T, lb = -GRB.INFINITY, name="y")
    z = model.addVars(T, name="z")
    y_list = y.tolist()

    model.addConstrs(
        (y[i] == beta - (stocks @ ret[i]) for i in range(T)), name="map_to_aux"
    )
    model.addConstrs(
        (z[i] == max_(y_list[i], 0) for i in range(T)), name="set_max"
    )


    Best regards,

    Maliheh

    1
  • yin pan
    • Gurobi-versary
    • First Comment

    i encounter a similar problem. the constriant is :

    and my code is:

    for t in range(T):
    for i in range(l_on+1, L):
    m.addConstr(y[t+1,0,i-1] == min_( max_( gp.quicksum(y[t,0,j] for j in range(l_on-1, i+1)) - min_( gp.quicksum(y[t,0,k] for k in range(l_on-1,L)) - theta, d_on[t] ), 0), y[t,0,i]+0))

    the error is:

    TypeError: unsupported operand type(s) for *: 'int' and 'GenExpr'

    could you please help me out? Thank you!

     

    1
  • Maliheh Aramon
    • Gurobi Staff

    Hi Yin, 

    The error traces back to the part below in your snippet because you are multiplying the integer -1 by a GenExpr.

    - min_(gp.quicksum(y[t,0,k] for k in range(l_on-1,L)) - theta, d_on[t])

    That being said, this is not the only issue with your snippet. 

    • You cannot model general expressions in a nested fashion as you already did. The y in an expression of the form x == op_(y) should be a Var, a list of Var and constants, or a tupledict of Var. It cannot be a GenExpr.
    • You need to define a separate auxiliary variable for each of the expressions below:
    1) gp.quicksum(y[t, 0, k] for k in range(l_on - 1, L)) - theta)
    2) min_(gp.quicksum(y[t,0,k] for k in range(l_on-1,L)) - theta, d_on[t])
    3) gp.quicksum(y[t,0,j] for j in range(l_on-1, i+1)) - min_(gp.quicksum(y[t,0,k] for k in range(l_on-1,L)) - theta, d_on[t])
    4) max_(gp.quicksum(y[t,0,j] for j in range(l_on-1, i+1)) - min_(gp.quicksum(y[t,0,k] for k in range(l_on-1,L)) - theta, d_on[t]), 0)

    The snippet below shows in detail how to do this.

    x_1 = m.addVars(T, name="x_1")
    x_2 = m.addVars(T, name="x_2")
    x_3 = m.addVars(T, L, name="x_3")
    x_4 = m.addVars(T, L, name="x_4")


    for t in range(T):
        m.addConstr(x_1[t] == gp.quicksum(y[t, 0, k] for k in range(l_on - 1, L)) - theta)
        m.addConstr(x_2[t] == gp.min_(x_1[t], d_on[t]))
        
        for i in range(l_on + 1, L):
            m.addConstr(
                x_3[t, i]
                == gp.quicksum(y[t, 0, j] for j in range(l_on - 1, i + 1)) - x_2[t]
            )
            m.addConstr(x_4[t, i] == gp.max_(x_3[t, i], 0))
            m.addConstr(
                y[t + 1, 0, i - 1]
                == gp.min_(
                      x_4[t, i],
                      y[t, 0, i],
                )
           )
    As a side note, in your formula \(\theta\) depends on \(t\), however, in the code snippet, it is a scalar constant. You might want to double check this.
     
    Best regards,
    Maliheh
    1
  • yin pan
    • Gurobi-versary
    • First Comment

    Hi Maliheh，

    It works. Thank you so much. You've made my day a good one!

    Have a GREAT day! 

    Best regards,
    Yin
    0

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