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Tensor[QuadraticFormSignature] - find the signature of a covariant, symmetric, rank 2 tensor

Calling Sequences

     QuadraticFormSignature(Q, 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 tensor Qis defined

     option  - keyword argument output = "dimensions"

     

 

Description

Examples

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 allThe 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.

 

(2.1)

 

First quadratic form.

(2.2)

(2.3)

We see that the quadratic form is positive-definite in all directions; it is a Riemannian metric.

 

Second quadratic form.

M > 

(2.4)
M > 

(2.5)

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.

M > 

(2.6)
M > 

(2.7)

The quadratic form is positive-definite in the 2 directions [ and negative-definite in the 2 directions [- .

 

Fourth quadratic form.

M > 

(2.8)
M > 

(2.9)

 

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.

M > 

(2.10)

 

Example 2.

We calculate the signature of the quadratic forms restricted to some subspaces.

 

M > 

(2.11)
M > 

(2.12)

 

Example 3.

Here we consider quadratic forms which depend upon a parameter.

 

M > 

(2.13)

(2.14)

Without further information on a it is not possible to compute the signature, that changes depending on the sign of

M > 

(2.15)
M > 

(2.16)
M > 

(2.17)

 

For more complicated examples, use infolevel to trace the testing performed by the QuadraticFormSignature procedure to see exactly at what point in the algorithm the procedure returns .

M > 

(2.18)
M > 

(2.19)

 

Now set the infolevel to 2.

M > 

(2.20)
M > 

The null space of the metric is

   Testing vector: D_x1
      The norm of this vector is: a
   Testing vector: D_x2
      The norm of this vector is: -1
   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
   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
   Testing vector: D_x1+D_x2
      The norm of this vector is: a+1

(2.21)

 

We see that the signature depends on the sign of  

M > 

(2.22)
M > 

(2.23)
M > 

(2.24)

See Also

DifferentialGeometry

Tensor

DGGramSchmidt

SubspaceType

 


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