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Tensor[QuadraticFormSignature] - find the signature of a covariant, symmetric, rank 2 tensor
Calling Sequences
QuadraticFormSignature(, B, option)
Parameters
Q - a covariant, symmetric, rank 2 tensor, possibly degenerate
B - (optional) a list of vectors spanning a subspace of the vector space upon which the tensoris defined
option - keyword argument = "dimensions"
Description
Let be a vector space and let be a covariant, symmetric, rank 2 tensor (quadratic form) defined on The null space of is defined to be for all The vector space decomposes into a direct sum
where is positive-definite on and negative-definite on that is, for alland for allEquality holds if and only if . The subspace is unique but the subspaces and are not unique. Nevertheless, Sylvester's law of inertia states that the dimensions of and are uniquely determined by .
The quadratic form is called non-degenerate if 0. In this case the dimensions of and specify the signature of
The command returns either a list of lists of vectors spanning the subspaces or a list of their dimensions.
The algorithm that is used is as follows. Let be a basis for . First, calculate the null space of Use the command ComplementaryBasis to find vectors such that
Search through the set of vectors {to find a vector Let be the orthogonal complement of by Write If then or Continue in this way to arrive at (*). Note that this method does not require the calculation of the eigenvalues/eigenvectors of
If the quadratic form depends upon parameters, then the assuming facility may be useful in determining the sign of at each step in the algorithm.
Examples
Example 1.
Find the signature of 4 different quadratic forms defined on the tangent space at a point of a 4-dimensional manifold.
First quadratic form.
We see that the quadratic form is positive-definite in all directions; it is a Riemannian metric.
Second quadratic form.
The quadratic form is positive-definite in the 3 directions [ and negative-definite in the 1 direction ; it is a Lorentzian metric.
Third quadratic form.
The quadratic form is positive-definite in the 2 directions [ and negative-definite in the 2 directions [- .
Fourth quadratic form.
The quadratic form is positive-definite in the 2 directions [ and negative-definite in the direction [ and degenerate in the direction [. Here are the dimensions of these spaces.
Example 2.
We calculate the signature of the quadratic forms restricted to some subspaces.
Example 3.
Here we consider quadratic forms which depend upon a parameter.
In this simple example, it is clear that the signature will change depending on the sign of
For more complicated examples, use infolevel to trace the testing performed by the procedure to see exactly at what point in the algorithm the procedure returns .
Now set the infolevel to 2.
The null space of the metric is
Searching for non-null vector in subspace: [D_x1, D_x2, D_x3, D_x4] Testing vector: D_x1 The norm of this vector is: a Testing vector: D_x2 The norm of this vector is: -1 Searching for non-null vector in subspace: [D_x1+D_x2, D_x3, D_x4] Testing vector: D_x1+D_x2 The norm of this vector is: a+1 Testing vector: D_x3 The norm of this vector is: 1 Searching for non-null vector in subspace: [D_x1+D_x2, D_x4] Testing vector: D_x1+D_x2 The norm of this vector is: a+1 Testing vector: D_x4 The norm of this vector is: 1 Searching for non-null vector in subspace: [D_x1+D_x2] Testing vector: D_x1+D_x2 The norm of this vector is: a+1
We see that the signature depends on the sign of .
See Also
DifferentialGeometry, Tensor, DGGramSchmidt, SubspaceType
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