Incompatible Dimensions?

回答済み

Victor Miller

2022年01月06日 15:03

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I'm using Gurobi 9.1.2 in Python.  The program below yields the message "Incompatible Dimensions".  This looks like a bug to me.  How do I add things with scalar variables to results from vector variables?

import gurobipy as gp
import numpy as np

model = gp.Model()
xvars = model.addMVar((10, 10), vtype = gp.GRB.CONTINUOUS, name = 'x')
yvar = model.addVar(vtype = gp.GRB.CONTINUOUS, name = 'y')
coeff = np.arange(1, 11)

first = coeff @ xvars[: , -1]
print(first.shape)
lin = first + yvar

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• 正式なコメント

  

  Simranjit Kaur

  • Gurobi Staff

  2025年11月16日 23:46

  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.
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  Eli Towle

  • Gurobi Staff

  2022年01月07日 00:34 編集済み

  Operations between an object from the matrix-friendly interface (MVar, MLinExpr, MQuadExpr) and an object from the term-based modeling interface (Var, LinExpr, QuadExpr) are largely unsupported. In your example, \( \texttt{first} \) is an MLinExpr object and \( \texttt{yvar} \) is a Var object.

  One way to resolve the issue is to define \( \texttt{yvar} \) as an MVar by adding it to the model with Model.addMVar():

  yvar = model.addMVar(shape=(1,), name='y')

  Note that you can use MVar() to create an MVar object from a list of Var objects, and MVar.tolist() to create a list of Var objects from an MVar object. These conversions can be useful in situations like yours. For example, an alternative solution is as follows:

  lin = first + gp.MVar(yvar)

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  Victor Miller

  2022年01月07日 20:15

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  Eli, Thanks for the answer.  As a related question, is there a convenient way to sum MVars along an axis (as in numpy)?

   

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  Eli Towle

  • Gurobi Staff

  2022年01月12日 04:36

  Unfortunately, there isn't currently a built-in way to sum an MVar along an axis. As a workaround, you could create a numpy array of Var objects by leveraging the MVar.tolist() method, then sum along an axis with ndarray.sum():

  >>> x = model.addMVar(shape=(3,3), name='x')
  >>> model.update()
  >>> xarr = np.array(x.tolist())
  >>> xarr.sum(axis=1)
  array([<gurobi.LinExpr: x[0] + x[1] + x[2]>,
         <gurobi.LinExpr: x[3] + x[4] + x[5]>,
         <gurobi.LinExpr: x[6] + x[7] + x[8]>], dtype=object)

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