# Tag Info

27

This is not really an answer to your question, essentially because there isn't (currently) a question in your post, but it is too long for a comment. Your statement that A co-ordinate transformation is linear map from a vector to itself with a change of basis. is muddled and ultimately incorrect. Take some vector space $V$ and two bases $\beta$ and ...

26

A (rank 2 contravariant) tensor is a vector of vectors. If you have a vector, it's 3 numbers which point in a certain direction. What that means is that they rotate into each other when you do a rotation of coordinates. So that the 3 vector components $V^i$ transform into $$V'^i = A^i_j V^j$$ under a linear transformation of coordinates. A tensor is a ...

26

The simplest way to explain the Christoffel symbol is to look at them in flat space. Normally, the laplacian of a scalar in three flat dimensions is: $$\nabla^{a}\nabla_{a}\phi = \frac{\partial^{2}\phi}{\partial x^{2}}+\frac{\partial^{2}\phi}{\partial y^{2}}+\frac{\partial^{2}\phi}{\partial z^{2}}$$ But, that isn't the case if I switch from the $(x,y,z)$ ...

24

You can decompose a rank two tensor $X_{ab}$ into three parts: $$X_{ab} = X_{[ab]} + (1/n)\delta_{ab}\delta^{cd}X_{cd} + (X_{(ab)}-1/n \delta_{ab}\delta^{cd}X_{cd})$$ The first term is the antisymmetric part (the square brackets denote antisymmetrization). The second term is the trace, and the last term is the trace free symmetric part (the round brackets ...

23

A second-order tensor can be represented by a matrix, just as a first-order tensor can be represented by an array. But there is more to the tensor than just its arrangement of components; we also need to include how the array transforms upon a change of basis. So tensor is an n-dimensional array satisfying a particular transformation law. So, yes, a ...

19

The connection is chosen so that the covariant derivative of the metric is zero. The vanishing covariant metric derivative is not a consequence of using "any" connection, it's a condition that allows us to choose a specific connection $\Gamma^{\sigma}_{\mu \beta}$. You could in principle have connections for which $\nabla_{\mu}g_{\alpha \beta}$ did not ...

16

Matrices are often first introduced to students to represent linear transformations taking vectors from $\mathbb{R}^n$ and mapping them to vectors in $\mathbb{R}^m$. A given linear transformation may be represented by infinitely many different matrices depending on the basis vectors chosen for $\mathbb{R}^n$ and $\mathbb{R}^m$, and a well-defined ...

14

The dual of a tensor you refer to is the Hodge dual, and has nothing to do with the dual of a vector. The word "dual" is used in too many different contexts, and in this case it is even used the same $*$ symbol. One usually specifies "Hodge dual", or "Hodge star operator", to avoid confusion. Both these "duals" are isomorphisms between vector spaces endowed ...

10

A (higher) $n$-rank tensor $T^{\mu_1\ldots \mu_n}$ with $n\geq 3$ cannot always be decomposed into just a totally symmetric and a totally antisymmetric piece. In general, there will also be components of mixed symmetry. The symmetric group $S_n$ acts on the indices $$(\mu_1,\ldots ,\mu_n)\quad \longrightarrow\quad (\mu_{\pi(1)},\ldots ,\mu_{\pi(n)})$$ via ...

9

A matrix is a special case of a second rank tensor with 1 index up and 1 index down. It takes vectors to vectors, (by contracting the upper index of the vector with the lower index of the tensor), covectors to covectors (by contracting the lower index of the covector with the upper index of the tensor), and in general, it can take an m upper/n-lower tensor ...

9

In a class I'm lecturing, I mention to my students (in a very, very elementary way) that vectors and covectors do not live in the same space. It's a typical school phrase... "Do not add apples and pears", and it's true! If you keep in mind the custom column and row representation of a vector, you can prove that both of them (by themselves) satisfy the ...

9

I) Let us for simplicity discuss tensors in the context of (finite-dimensional) vector spaces and multilinear algebra. [There is a straightforward generalization to manifolds and differential geometry.] II) Abstractly in coordinate-free notation, the Kronecker delta tensor, or tensor contraction, is the natural pairing $$\tag{1} V \otimes ... 8 A physicist would write your first equation x^a = x^\mu e_\mu^a. The notation x^a is invariant in your terminology. The a is an abstract index. It is ostensibly not supposed to be thought of as ranging over a set of numerical values, but is just a marker that indicates that x is a vector (i.e., rank 1,0 tensor.) Similarly for each \mu, e^a_\mu is ... 8 Each of the indices in a tensor have a particular left-right ordering. This ordering cannot be changed unless the tensor has some particular symmetry that permits it (or rather, that equates different components on interchange). The up-down positions of indices tells us about whether the index is associated with using a basis vector (up) or a basis ... 8 The covariant derivative is metric compatible, so \nabla_{\alpha} g_{\beta \gamma} = 0. This is the condition that the inner product is preserved under parallel transport. 7 The notion of covariant derivative is equivalent to the notion of connection. More precisely, for every connection \nabla and vector field X, the operation \nabla_X is a covariant derivative. Connections \nabla on the tangent bundle TM of a manifold are usually induced by a metric, this is the so called Levi-Citiva connection. It is essentially ... 6 The earliest instance I have found is Minkowski's "Die Grundgleichungen für die elektromagnetischen Vorgänge in bewegten Körpern" in "Nachrichten von der Georg-Augusts-Universität und der Königl. Gesellschaft der Wissenschaften zu Göttingen" from 1908. A digitized version is found at ... 6 Physicists are always interested in what properties of a physical system are invariant under symmetries. If it's tricky to see the symmetry then they'll rearrange the system to make the symmetry more obvious. For example, consider a covariant rank two tensor like T^{ab}. In general the components of this tensor will change if the tensor is rotated in 3D. ... 6 Let there be given a manifold (M,\nabla) equipped with a (not necessarily torsionfree) tangent bundle connection \nabla. I got the (possibly faulty) impression from reading the first lines in OP's question formulation (v18) that OP is asking: Is it possible that the local coordinate expression for the covariant derivative of a co-vector/one-form ... 6 I'll write this as an answer so that the math is more clear. So given an (p,q)-tensor T^{\mu_1\cdots\mu_p}{}_{\nu_1\cdots\nu_q}, this one transforms as:$$T'^{\mu'_1\cdots\mu'_p}{}_{\nu'_1\cdots\nu'_q}=\frac{\partial x^{\mu'_1}}{\partial x^{\mu_1}}\cdots \frac{\partial x^{\mu'_p}}{\partial x^{\mu_p}}\frac{\partial x^{\nu_1}}{\partial ...

5

Equation (13) expresses the metric on an embedded hypersurface given by the relations $y^k = y^k(x^a)$. However, the equation for the inverse metric (4-th equation) is in general not correct. Take for example a hypersurface defined by: $y^1 = x^1$, $y^2 = x^2$, $y^3 = x^2$. In our case, the partial derivative of $x^2$ with respect to $y^2$ or $y^3$ ...

5

What you said is only true if the hypersurface is space-like or time-like. If a non-null hypersurface is defined by $f(x) =$ constant, then the normal to the hypersurface is given by $$n_\alpha \propto \partial_\alpha f$$ The fact that the hypersurface is non-null implies $$g^{\alpha\beta} \partial_\alpha f \partial_\beta f = \varepsilon\neq 0$$ ...

5

There are two more points that can be made here. Sorry if I repeat someone. In a way you are right that if you have a vector space and its dual there is no intrinsic way to say which space is the original and which is the dual. This is because there is a canonical isomorphism between a vector space and the dual of its dual. In other words if $V$ is a vector ...

5

For doing matrix multiplication, yes, we usually put the dummy indicies together, but this is (assuming one is not working over $\mathbb{H}$) convention really. Don't forget that this is shorthand Einstein convention. When you write $$\mathbf{A} \cdot \mathbf{B} = A_{ij} B_{jk} = \sum_{j=1}^n A_{ij} B_{jk}$$ you are writing a shorthand version in ...

5

Gradient is covariant! Why? The components of a vector contravariant because they transform in the inverse (i.e. contra) way of the vector basis. It is customary to denote these components with an upper index. So, if your coordinates are called $q$'s, they are denoted $q^i$. Therefore, the gradient (or a derivative if you prefer) is $$\partial_i = ... 5 \nabla_\sigma is the covariant derivative. \nabla^\sigma means g^{\sigma\rho}\nabla_\rho. It's pretty much the same as raising any other index. The covariant derivative when acting on any tensor adds a down index, and you can raise it as with any other index. Since the covariant derivative of the metric is 0, you can work with either \nabla_\sigma or ... 5 A variation of a tensor is always a tensor and the formula for the value above doesn't show otherwise. What you probably find surprising is that \delta g_{\mu\nu} and \delta g^{\rho\sigma} are not related to each other by simply raising the indices \mu,\nu or lowering the indices \rho,\sigma. Indeed, they're not related in this way. In this case, ... 5 Note that there is no physical meaning of Christoffel symbols as they are not tensors. It's always possible to choose local coordinates such that all of \Gamma vanish. But their mathematical meaning is that they form a pseudotensor. Technically, if we have two covariant derivatives \nabla_1 and \nabla_2 then their difference \Gamma := \nabla_1 - ... 5 An easy way to see that they are distinct is to consider what happens upon raising (or lowering) all indices. For example, upon lowering,$$ T_{ab}{}^{cde} $$becomes T_{abcde}, whereas$$ T_{a}{}^{cd}{}_{b}{}^{e} $$becomes T_{acdbe}, and similarly$$ T_{a}{}^{cde}{}_{b} $$becomes$$ T_{acdeb}. $$You need to "slant" the indices so as to keep track ... 5 No. Definitions: A rank-n tensor is a linear map from n vectors to a scalar. A symmetric tensor is one which in which the order of the arguments doesn't matter. An antisymmetric tensor in which transposing two arguments multiplies the result by -1. Suppose we have some rank-3 tensor T with symmetric part S and anti-symmetric part A so$$T(a,b,c) ...

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