## Archive for **March 2012**

## A Note on Discrete Helmholtz Decomposition

*The following is a note I sent to my PhD advisor, Professor Weng Cho Chew, on September 13, 2011 after a discussion over dinner as he was headed back to UIUC from a 4-year stint as the Dean of Engineering at the University of Hong Kong.*

### Decomposing Finite Dimensional Inner Product Spaces

Given finite-dimensional inner product spaces , and a linear map , the adjoint map is the unique linear map satisfying the property

for all and .

In this section, we show that can be decomposed into two orthogonal subspaces

This is a fairly simple exercise as any finite-dimensional inner product space can be decomposed into a subspace and its orthogonal complement, i.e.

.

The only thing to show is that .

To do this, note whenever , then

for all . Thus, is also in , i.e. . Similarly, whenever , then

for all . Thus, is also in , i.e. . Since both and , it follows that .

### Hodge-Helmholtz Decomposition

Given finite-dimensional inner product spaces , , and linear maps , such that , we wish to show that the inner product space may be decomposed into three orthogonal subspaces

,

where .

To show this, note that if , then

but this implies and . Conversely, if and , then is trivially in . In other words,

Finally, since , we also have . Consequently, when , then so

Applying the decomposition from the previous section twice, we conclude that

and since , it follows that

which may be expressed simply as

Putting this together we see the desired Hodge-Helmholtz decomposition

### Computational Electromagnetics

The preceding discussion is quite general and holds for any finite-dimensional inner product spaces , , and any linear maps , satisfying . In this section, we specialize to computational electromagnetics.

Consider a discretization of a surface consisting of vertices, directed edges, and oriented triangular faces. If we associate a degree of freedom to each vertex, the span of these degrees of freedom form an -dimensional vector space . Associating a degree of freedom to each directed edge forms an -dimensional vector space and associating a degree of freedom to each oriented face forms an -dimensional vector space . For concreteness, vectors in will be expanded via

where denotes the degree of freedom on the i^{th} vertex, vectors in will be expanded via

where denotes the degree of freedom on the i^{th} directed edge, and vectors in will be expanded via

where denotes the degree of freedom on the i^{th} oriented face.

To turn , , and into inner product spaces, we need to define three respective inner products. This can be done by defining three sets of basis functions , , and . and take values defined at vertices and faces, respectively, and maps these to functions defined over each face. Similarly, takes values defined along each edge and maps these to vector fields defined over each face.

The basis functions linearly turn vectors in , , and into functions and vector fields defined over the surface via

and

The inner products may then be defined in terms of basis functions via

and

Letting , , and denote column matrix representations of vectors in , , and , the inner products may be expressed in terms of matrix-vector products via

and

The matrix-vector representation is helpful for explicitly expressing the adjoint of a linear map via

so that

Similarly, the adjoint of a linear map may be represented in matrix form via

In computational electromagnetics, a fundamental linear map is the exterior derivative, which will be denoted for . Since , , and are finite dimensional, has a sparse matrix representation .

For the sake of interpretation, the matrix may be thought of as the gradient along the respective directed edge, may be thought of as the curl of the edge vector field around each oriented face, may be thought of as the transverse gradient[1] across each directed edge, and may be thought of as the divergence of the edge vector field.

Critically note,

As a result, we have the inner product space of edge vector fields decomposes into

where . In other words, any edge vector may be expressed as

for some , , and . The above may be thought of as a discrete version of Hodge-Helmholtz decomposition for computational electromagnetics.

### References

This note is an informal (and quickly drafted) document intended to help explain Hodge-Helmholtz decomposition in computational electromagnetics. No claim of any original content is intended and a proper literature search was not performed. For pointers to some related material with more complete references, see the following:

- PyDEC: Software and Algorithms for Discretization of Exterior Calculus
- Least Squares Ranking on Graphs

[1] If the degree of freedom associated to an oriented face is interpreted as the magnitude of vector normal to the face, may be thought of as the curl of this normal vector field along the directed edge.