# Information-Theoretic Entropy Approximants

Shannon  introduced the concept of entropy in information theory, with an eye on its applications in communication theory. The general form of informational entropy (Shannon-Jaynes or relative entropy functional) is [2,3,4]: (1)

where is a -estimate (prior distribution). The quantity is also referred to as the Kullback-Leibler (KL) distance. As a means for least-biased statistical inference in the presence of testable constraints, Jaynes's used the Shannon entropy to propose the principle of maximum entropy , and if the KL-distance is adopted as the objective functional, the variational principle is known as the principle of minimum relative entropy .

Consider a set of distinct nodes in that are located at ( ), with denoting the convex hull of the nodal set. For a real-valued function , the numerical approximation for is: (2)

where , is the basis function associated with node , and are coefficients. The use of basis functions that are constructed independent of an underlying mesh has become popular in the past decade--meshfree Galerkin methods are a common target application for such approximation schemes [6,7,8,9,10]. The construction of basis functions using information-theoretic variational principles is a new development [11,12,13,14]; see Reference  for a recent review on meshfree basis functions. To obtain basis functions using the maximum-entropy formalism, the Shannon entropy functional (uniform prior) and a modified entropy functional (Gaussian prior) were introduced in References  and , respectively, which was later generalized by adopting the Shannon-Jaynes entropy functional (any prior) . The implementation of these new basis functions has been carried out, and this manual describes a Fortran 90 library for computing maximum-entropy (max-ent) basis functions and their first and second derivatives for any prior weight function.

We use the relative entropy functional given in Eq. (1) to construct max-ent basis functions. The variational formulation for maximum-entropy approximants is: find as the solution of the following constrained (convex or concave with or , respectively) optimization problem: where is the non-negative orthant, is a non-negative weight function (prior estimate to ), and the linear constraints form an under-determined system. On using the method of Lagrange multipliers, the solution of the variational problem is : (4)

where ( ) are shifted nodal coordinates, are the Lagrange multipliers (implicitly dependent on the point ) associated with the constraints in Eq. (3c), and is known as the partition function in statistical mechanics. The smoothness of maximum-entropy basis functions for the Gaussian prior was established in Reference ; the continuity for any ( ) prior was proved in Reference .

N. Sukumar