Python Matrix-Friendly API Functions#

Implements a small set of functions equivalent to numpy that operate on the array-like matrix-friendly API classes MVar, MLinExpr, MQuadExpr, MConstr, MQConstr, and MGenConstr.

hstack(tup)#

Stack arrays in sequence horizontally (column wise). This is equivalent to concatenation along the second axis, except for 1-D arrays where it concatenates along the first axis.

This method has the same behavior as numpy.hstack, except that it always returns a gurobipy matrix-friendly API object of the appropriate type.

Parameters:

tup – A sequence of matrix-friendly API objects

Returns:

Returns a matrix-friendly API object. Note that the return type depends on the types of the input objects.

Example:
X = model.addMVar((k, n))
Y = model.addMVar((k, m))
XY = gp.hstack((X, Y))     # (k, n+m) MVar
vstack(tup)#

Stack arrays in sequence vertically (row wise). This is equivalent to concatenation along the first axis after 1-D arrays of shape (N,) have been reshaped to (1,N).

This method has the same behavior as numpy.vstack, except that it always returns a gurobipy matrix-friendly API object of the appropriate type.

Parameters:

tup – A sequence of matrix-friendly API objects

Returns:

Returns a matrix-friendly API object. Note that the return type depends on the types of the input objects.

Example:
X = model.addMVar((n, k))
Y = model.addMVar((m, k))
XY = gp.vstack((X, Y))     # (n+m, k) MVar
concatenate(tup, axis)#

Join a sequence of arrays along an existing axis.

This method has the same behavior as numpy.concatenate, except that it always returns a gurobipy matrix-friendly API object of the appropriate type.

Parameters:

  • tup – A sequence of matrix-friendly API objects

  • axis – The axis along which the arrays will be joined. If axis is None, arrays are flattened before use. Default is 0.

Returns:

Returns a matrix-friendly API object. Note that the return type depends on the types of the input objects.

Example:
X = model.addMVar((k, n))
Y = model.addMVar((k, m))
XY = gp.concatenate((X, Y), axis=1)  # (k, n+m) MVar