Index: The Book of Statistical ProofsGeneral Theorems ▷ Information theory ▷ Shannon entropy ▷ Log sum inequality

Theorem: Let $a_1, \ldots, a_n$ and $b_1, \ldots, b_n$ be non-negative real numbers and define $a = \sum_{i=1}^{n} a_i$ and $b = \sum_{i=1}^{n} b_i$. Then, the log sum inequality states that

\[\label{eq:logsum-ineq} \sum_{i=1}^n a_i \, \log_c \frac{a_i}{b_i} \geq a \, \log_c \frac{a}{b} \; .\]

Proof: Without loss of generality, we will use the natural logarithm, because a change in the base of the logarithm only implies multiplication by a constant:

\[\label{eq:log-ln} \log_c a = \frac{\ln a}{\ln c} \; .\]

Let $f(x) = x \ln x$. Then, the left-hand side of \eqref{eq:logsum-ineq} can be rewritten as

\[\label{eq:logsum-ineq-s2} \begin{split} \sum_{i=1}^n a_i \, \ln \frac{a_i}{b_i} &= \sum_{i=1}^n b_i \, f\left( \frac{a_i}{b_i} \right) \\ &= b \sum_{i=1}^n \frac{b_i}{b} \, f\left( \frac{a_i}{b_i} \right) \; . \end{split}\]

Because $f(x)$ is a convex function and

\[\label{eq:sum-bi-b} \begin{split} \frac{b_i}{b} &\geq 0 \\ \sum_{i=1}^n \frac{b_i}{b} &= 1 \; , \end{split}\]

applying Jensen’s inequality yields

\[\label{eq:logsum-ineq-s3} \begin{split} b \sum_{i=1}^n \frac{b_i}{b} \, f\left( \frac{a_i}{b_i} \right) &\geq b \, f\left( \sum_{i=1}^n \frac{b_i}{b} \frac{a_i}{b_i} \right) \\ &= b \, f\left( \frac{1}{b} \sum_{i=1}^n a_i \right) \\ &= b \, f\left( \frac{a}{b} \right) \\ &= a \, \ln \frac{a}{b} \; . \end{split}\]

Finally, combining \eqref{eq:logsum-ineq-s2} and \eqref{eq:logsum-ineq-s3}, this demonstrates \eqref{eq:logsum-ineq}.


Metadata: ID: P165 | shortcut: logsum-ineq | author: JoramSoch | date: 2020-09-09, 02:46.