How do I multiply an array and a 2-D MVar object using the matrix-friendly Python API? – Gurobi Help Center

If you are using Gurobi 10

Operations on 2D MVar work natively, following the dimensionality conventions of numpy. For example, let \( a \in \mathbb{R}^m \), let \(b \in \mathbb{R}^n \), and let \(X\) be an \( m \times n \) MVar object. We can model the expression \( a^\top X b \) as follows:

import gurobipy as gp
import numpy as np

model = gp.Model()
X = model.addMVar((3,4))

a = np.random.rand(3)
b = np.random.rand(4)

model.setObjective(a @ X @ b)

If you are using Gurobi 9.x

In Gurobi version 9.x, operations with 2-D MVar objects were largely unsupported. Attempting to construct the expression \( a^\top X b \) with the above code results in GurobiError: Variable is not a 1D MVar object:

Matrix linear expressions could only be constructed from 1-D MVar objects. For example, the expression \( a^\top X b \) is built by slicing the 2-D \( X \) matrix variable down to single dimensions, then accumulating the result:

import gurobipy as gp
import numpy as np

model = gp.Model()
X = model.addMVar((3,4))

a = np.random.rand(3)
b = np.random.rand(4)

model.setObjective(sum(b[j] * a @ X[:, j] for j in range(4)))

Note: Gurobi 9.0 introduced the first version of the Python matrix API. The developers are continually working to improve the usability of the matrix API.

Further information

If you are using Gurobi 10

If you are using Gurobi 9.x

Further information

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