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  • Research Article
  • Open Access

Solution to a Function Equation and Divergence Measures

Advances in Difference Equations20112011:617564

  • Received: 1 January 2011
  • Accepted: 11 February 2011
  • Published:


We investigate the solution to the following function equation , which arises from the theory of divergence measures. Moreover, new results on divergence measures are given.


  • Machine Learning
  • Functional Equation
  • Orthonormal Base
  • Divergence Measure
  • Statistical Inference

1. Introduction

As early as in 1952, Chernoff [1] used the -divergence to evaluate classification errors. Since then, the study of various divergence measures has been attracting many researchers. So far, we have known that the Csiszár -divergence is a unique class of divergences having information monotonicity, from which the dual geometrical structure with the Fisher metric is derived, and the Bregman divergence is another class of divergences that gives a dually flat geometrical structure different from the -structure in general. Actually, a divergence measure between two probability distributions or positive measures have been proved a useful tool for solving optimization problems in optimization, signal processing, machine learning, and statistical inference. For more information on the theory of divergence measures, please see, for example, [25] and references therein.

Motivated by these studies, we investigate in this paper the solution to the following function equation
which arises from the discussion of the theory of divergence measures, and show that for , if ,  , and satisfy

then is the solution of a linear homogenous differential equation with constant coefficients. Moreover, new results on divergence measures are given.

Throughout this paper, we let be the set of real numbers and are a convex set.

Basic notations: ; is strictly convex and twice differentiable; is differentiable injective map; is the general vector Bregman divergence; is strictly convex twice-continuously differentiable function satisfying ;   is the vector -divergence.

If for every ,

then we say the or is in the intersection of -divergence and general Bregman divergence.

For more information on some basic concepts of divergence measures, we refer the reader to, for example, [25] and references therein.

2. Main Results

Theorem 2.1.

Assume that there are differentiable functions
and such that
Then and

for some .


Since is differentiable functions, it is clear that
Then is a finite dimension space. So we can find differentiable functions
as the orthonormal bases of , where . Observing that
we have
Next we prove that
It is easy to see that we only need to prove the following fact:
Actually, if this is not true, that is,
then there exists such that
we get
that is,
Since is linearly independent, we see that
This is a contradiction. Hence (2.12) holds, and so does (2.11). Thus, there are such that
So we have
Let , and
Since is a symmetric matrix, we have
for an orthogonal matrix , and a diagonal matrix
So, for all ,
Without loss the generalization, we can assume that
Thus, for all ,
By the similar arguments as above, we can prove
So there is a matrix satisfying
By mathematical induction we obtain

So .

be the annihilation polynomial of . Then
Since , we can find such that

The proof is then complete.

Theorem 2.2.

Let the -divergence be in the section of -divergence and general Bregman divergence. Then satisfies

for some .


If are in the intersection of -divergence and general Bregmen divergence, then we have

Thus, a modification of Theorem 2.1 implies the conclusion.

Moreover, it is not so hard to deduce the following theorem.

Theorem 2.3.

Let a vector -divergence is are the intersection of vector -divergence and general Bregman divergence and satisfy

where is strictly monotone twice-continuously differentiable functions. Then the divergence is -divergence or vector -divergence times a positive constant .



This work was supported partially by the NSF of China and the Specialized Research Fund for the Doctoral Program of Higher Education of China.

Authors’ Affiliations

Department of Mathematics, Shanghai Jiao Tong University, Shanghai, 200240, China


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© C.-L. Dong and J. Liang. 2011

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.