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By the usual subtyping rule for function types, this means that the overriding method should return a more specific type (return type covariance) and accept a more general argument (parameter type contravariance). In UML notation, the possibilities are as follows (where Class B is the subclass that extends Class A which is the superclass):
C# supports return type covariance as of version 9.0. [1] Covariant return types have been (partially) allowed in the Java language since the release of JDK5.0, [ 2 ] so the following example wouldn't compile on a previous release:
Contravariance of method parameter types in the subtype. Covariance of method return types in the subtype. New exceptions cannot be thrown by the methods in the subtype, except if they are subtypes of exceptions thrown by the methods of the supertype. In addition to the signature requirements, the subtype must meet a number of behavioural ...
Covariance and contravariance may refer to: Covariance and contravariance of vectors, in mathematics and theoretical physics; Covariance and contravariance of functors, in category theory; Covariance and contravariance (computer science), whether a type system preserves the ordering ≤ of types; An informal synonym for invariance (physics)
The duality between covariance and contravariance intervenes whenever a vector or tensor quantity is represented by its components, although modern differential geometry uses more sophisticated index-free methods to represent tensors. In tensor analysis, a covariant vector varies more or less reciprocally to a corresponding contravariant vector ...
As an example, a mixed tensor of type (1, 2) can be obtained by raising an index of a covariant tensor of type (0, 3), =, where is the same tensor as , because =, with Kronecker δ acting here like an identity matrix.
The explicit form of a covariant transformation is best introduced with the transformation properties of the derivative of a function. Consider a scalar function f (like the temperature at a location in a space) defined on a set of points p, identifiable in a given coordinate system , =,, … (such a collection is called a manifold).
In probability theory and statistics, the covariance function describes how much two random variables change together (their covariance) with varying spatial or temporal separation. For a random field or stochastic process Z ( x ) on a domain D , a covariance function C ( x , y ) gives the covariance of the values of the random field at the two ...