MQuadExpr
Gurobi quadratic matrix expression object. Quadratic matrix expressions are used to build quadratic objective functions and quadratic constraints. They are temporary objects that typically have short lifespans.
You generally build quadratic matrix expressions using overloaded
operators. For example, if x is an
MVar object and A is a 2-D
matrix (dense or sparse), then x @ A @ x and x @ x
are both MQuadExpr objects.
Arithmetic operations supported on MQuadExpr objects
are addition, subtraction (e.g.,
expr = x @ A @ x - y @ B @ y), and multiplication by a constant
(e.g. expr = 2 * x @ A @ y).
An MQuadExpr object has a shape representing its
dimensions, a size that counts the total number of elements,
and an ndim that gives the number of dimensions. These
properties lean on their counterparts in NumPy's ndarray class.
When working with MQuadExpr objects, you need to make sure that
the operands' shapes are compatible. The first step in building an
MQuadExpr is often to build an MLinExpr (e.g., |expr = x
@ A @ x|), so we refer you to the discussion of
MLinExpr shape rules first.
Rules for other operations on MQuadExpr objects are generally
similar to those for MLinExpr objects, and both classes follow
NumPy rules, so we refer you to the NumPy documentation for details.
The full list of overloaded operators
on MQuadExpr objects
is as follows:
+, +=,
-, -=, *, and *=.
In Python parlance, we've defined the following
QuadExpr functions:
__add__, __radd__, __iadd__,
__sub__, __rsub__, __isub__, __neg__,
__mul__, __rmul__, and __imul__.
We've also overloaded the comparison operators (==, <=, and >=), to make it easier to build constraints from quadratic expressions.
Subsections
