Why can't we contract two upper or two lower indices?
Can you explain this with the help of matrices?
Moreover, how can we represent the operation of a (0,2) tensor on vectors with a matrix?
Why can't we contract two upper or two lower indices?
Can you explain this with the help of matrices?
Moreover, how can we represent the operation of a (0,2) tensor on vectors with a matrix?
On
The basic reason is because we've generalised the usual inner product to include metric tensors other than the Kronecker delta. So instead of the squared length of the vector $x$ being $\sum_i (x^i)^2=\sum_{ij}\delta_{ij}x^ix^j$ as in Euclidean space, we can replace $\delta_{ij}$ with a real symmetric matrix $g_{ij}$. If this matrix has a mixture of positive and negative eigenvalues, as happens e.g. in special and general relativity with both space and time dimensions, a real rotation can't restore the Kronecker-delta metric, because the manifold isn't Euclidean. And even if you're working in a coordinate system that uses a Kronecker delta now, a coordinate transformation can spoil that. How does an infinitesimal $dx^a g_{ab}dx^b$ respond when we write in terms of $dy^c$, viz. $dx^a=L^a_{\: c} dy^c$ (say)? Well, obviously the metric tensor updates. And because $v_a:=g_{ab}v^b$ with a coordinate-dependent formula for the $g_{ab}$, we can't just write e.g. $v^a w^a$; we need $v^a w_a=v^a g_{ab}w^b$, whose invariances are easy to work out.
Well, you can contract two upper or two lower indices if you like. The problem is that the result will then be dependent on the co-ordinate system that you use, so it will not represent a physically meaningful quantity.
On the other hand, if you start with a (1,1) tensor and you contract the upper index with the lower index, then the dependencies on co-ordinate systems "cancel each other out" and you are left with a quantity that is independent of the co-ordinate system used i.e. a scalar.
To answer the second part of your question, a matrix can be used to represent a (1,1) tensor - a linear function that maps a vector and a co-vector to a scalar. A (0,2) tensor, on the other hand, maps a pair of vectors to a scalar so you can think of it as a covector, each component of which is another covector.