Matrixmultiplication with decisionmatrix

Answered

Yannik Strieben

November 19, 2020 15:13 Edited

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Hello,

in my optimization problem there is a decisionmatrix and two numpy arrays, initialized as follows

x = m.addMVar((3, 3), vtype=GRB.INTEGER, name="x")
a = np.ones((3, 3))
b = np.ones((3, 3))

Every element of x represents a dependence of row and column. So there are nine decision variables. Shapes and libs are correct.

I have got to determine the matrixproduct a*x*b. Later on all elements of the resulting matrix should be greater or equal to a lower bound matrix, something like that

for i in range(3):
    for j in range(3):
        m.addConstr((a @ x @ b)[i, j] >= lb[i, j])

There is an error by determine the product a*x*b. "Unable to convert argument to an expression."

I cant fix it, can anyone help?

0

• Official comment

  

  Simranjit Kaur

  • Gurobi Staff

  June 23, 2025 03:04 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?.
• 

  Eli Towle

  • Gurobi Staff

  November 19, 2020 23:24 Edited

  Currently, you can only construct matrix linear expressions with 1-D MVar objects. Our development team is considering adding more support for 2-D MVar objects in a future release, since functionality like this has been requested a few times.

  Instead, you can add these constraints using matrix slicing:

  for i in range(3):
      for j in range(3):
          m.addConstr(sum(b[k, j] * a[i, :] @ x[:, k] for k in range(3)) >= lb[i, j])

  You could add all of the constraints in one line using Model.addConstrs(), but that might be difficult to read.

   

  1
• 

  Maliheh Aramon

  • Gurobi Staff

  December 20, 2022 23:15

  Hi Yannik,

   

  Gurobi 10.0 was recently released. Included in this release is an improved Python Matrix API which allows matrix operations on 2D MVar objects.

   

  The code snippet below runs fine using Gurobi 10.0.

  m = gp.Model()
  
  x = m.addMVar((3, 4), vtype=gp.GRB.INTEGER, name="x")
  A, B, b = np.random.rand(3, 3), np.random.rand(4, 3), np.random.rand(3, 3)
  
  m.addConstr(A @ x @ B <= b)

  Checkout the Matrix-friendly Modeling with Gurobipy webinar if you would like to learn more about this new functionality. 

   

  Please continue submitting new community posts for any bugs you might encounter in the future or for any comments/questions you might have. Users like you help to make Gurobi better!

   

  Best regards,

  Maliheh

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