In a limited context, the terms "Covariance"
and "Contravariance" arise in cases where you have a basis for a vector
space that is **NOT** orthonormal. In the restricted
context of the presence of a metric, one way to view covariance and
contravariance is to think of them as different basis descriptions of the
same object.

In the Euclidean case the metric components are:

Whether it be a vector or a tensor, there exists only
** one object**. Covariant and Contravariant are just different
ways of describing it. Sort of like CGS and MKS units in physics.
In your equations you have to know what "language" the object is described
in in order for the equations to "play" together. The "language"
is implied by the level of the indices.

Take 2-d Euclidean space as an example:

Let's assume we have a Euclidean orthonormally represented
vector **V** = <5,12>

Instead of the unit vectors *<1,0>* and *<0,1>*
lets define new basis vectors:

*
A _{1}*

Let's call this our contravariant basis.

**V**
= -26***A _{1}** + 24*

Our contravariant components of

Now let's define a new basis, that is *reciprocal*
to ** A_{1}**,

Notice that the Euclidean inner product between these
two bases is the identity matrix:

*A ^{i}*

In this new (covariant) basis we have:

**V**
= 2.5 **A ^{1}** + 9.75

Our covariant components of ** V** are now:

*V _{1}
= 2.50*

In both cases ** V** is still

Contravariant Covariant

**V**
= <5,12> = V^{1}**A _{1}** + V

In all cases, ** V** is still the same vector

In the Euclidean case the metric components are:

*G11 = 1*

*G12 = 0*

*G21 = 0*

*G22 = 1*

In the "Contravariant" case the metric components are:

*G _{11}
= A_{1} dot A_{1} = < 0.50,
0.0 > dot < 0.50, 0.0 > = 0.2500*

In the "Covariant" case the metric components are:

*G ^{11}
= A^{1} dot A^{1} = < 2, -3
> dot < 2, -3 > = 13*

Notice that *G _{ij}*

"Raising" or "Lowering" the indices refers to changing
the basis between

covariant and contravariant. This is done using
the metric.

* V _{j}
= V ^{i} * G_{ij} and*

* V ^{j}
= V_{i} * G ^{ij}*

where we sum over repeated indices.

**V** dot **V** = 5^{2} + 12^{2}
= 13^{2} = V ^{i} V ^{j} G _{ij} = V_{i}
V_{j} G ^{ij} = V ^{i
}V_{i }= V_{i} V ^{i}

*(note: be careful above. I'm mixing exponentials
and indices.)*

For another great web page check out Kevin Brown's exposition on this subject.

The terms contravariant and covariant apply to bases based on how they arise, and how the object's components transform between different coordinate systems. In the physical sciences "Contravariant" applies to vectors that arise from the derivative of the position vector, such as velocity and acceleration. Given that definition of contravariant, objects derived by taking the gradient have their coordinates expressed in the covariant coordinate "language".

A reminder, this conceptual view of the situation only applies to sets for which there is a metric, which includes General Relativity (in cases where you are not trying to solve for the metric). In actuality though, vectors and covectors are more generally defined than in the above conceptual model, but in the case of the existence of a metric, the model is equivalent and will take you a long way.

A more general geometric view of a covector is to think of it as a collection of parallel spaced infinite planes (in 3d Euclidean space, parallel lines in 2d Euclidean space, etc.) The magnitude is viewed as the plane density, for example, "how many parallel planes per inch." As mentioned above the gradient is an example of this.

Notice that when taking the inner product between
a vector and a covector **the metric is not used or needed. **Vectors
and Covectors, Contravariance and Covariance, are generally defined independent
of a metric. In the presence of a metric there is a "formula" that relates
the two. Without a metric the two are independent and unrelated.
The general definition of a covector is, "a linear real valued function
of a vector" or a function that takes in a vector and outputs a real number.
In the presence of a metric this is just the inner product. Without
a metric the term "inner product" is meaningless because metrical objects
such as angles and length are not defined.