Lagrangian Multiplier with Matrices

[CuppoJava]

Hi,
I'm trying to use calculus of variations to solve for the probability distribution with highest entropy for a given covariance matrix. I want to maximize this:

H[p(\vec{x})] = -\int p(\vec{x})*ln(p(\vec{x}))d\vec{x}

with the following constraints:

\int p(\vec{x}) = 1
\int \vec{x}p(\vec{x})d\vec{x} = \vec{u}
\int (\vec{x}-\vec{u})(\vec{x}-\vec{u})^{T}p(\vec{x})d\vec{x} = \Sigma

Using Lagrangian multipliers, the proposed maximization function is:

F[p(\vec{x})] = -\int p(\vec{x})*ln(p(\vec{x}))d\vec{x} + \lambda_{1}(\int p(\vec{x})d\vec{x}-1) + \vec{m}^{T}(\int \vec{x}p(\vec{x})d\vec{x} - \vec{u}) + Tr\{L(\int (\vec{x}-\vec{u})(\vec{x}-\vec{u})^{T}p(\vec{x})d\vec{x} - \Sigma)\}

I understand that m is needed because there is D constraints imposed by the mean. And L is needed because there is DxD constraints imposed by the covariances. But what is the trace operator doing in there?

Thanks for the help
-Patrick

 

[fresh_42]

The method by Lagrange multipliers involves an inner product for the constraints. The trace is such a product.