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Finite Pinsker inequality in Euclidean norm

Statement

Let P=(pi) and Q=(qi) be probability vectors on a finite nonempty set. With natural logarithms and D(P∥Q)=+∞ when P≪̸Q, one has D(P∥Q)≥12∥P−Q∥12≥12∥P−Q∥22.

Facts & Assumptions

Given: Finite probability vectors P,Q and the finite KL convention in the Statement.

[F1]

Finite log-sum inequality with support and equality cases: grouping coordinates into two classes cannot increase the finite KL divergence; the grouped divergence is at most the original divergence, including the stated support conventions.

[F2]

Finite Kullback–Leibler divergence: D(P∥Q) is the sum of piln⁡(pi/qi) over positive pi, and equals +∞ if a positive pi lies over qi=0.

Proof

1.1F2givencasesalgebra

If P≪̸Q, the first inequality is immediate because its left side is +∞. Otherwise let A={i:pi>qi} and d=P(A)−Q(A). Since ∑i(pi−qi)=0, the positive and negative coordinate differences have equal total magnitude, so ∥P−Q∥1=2d. If A is empty, then P=Q and the claim holds.

2.1F1F2step 1.1algebra

For A and its complement, [F1] gives D(P∥Q)≥pln⁡(p/q)+(1−p)ln⁡((1−p)/(1−q)), where p=P(A) and q=Q(A), with endpoint terms interpreted by the same support convention.

3.1F3F4step 2.1given

If q∈{0,1}, finite divergence and P≪Q force p=q, so the binary divergence is zero and d=0. For 0<q<1, define g(p)=pln⁡(p/q)+(1−p)ln⁡((1−p)/(1−q))−2(p−q)2. On 0<p<1, [F3] and the product rule give g(q)=g′(q)=0 and g′′(p)=1/[p(1−p)]−4≥0. Thus g is convex and minimized at q; continuity of tln⁡t at zero from [F4] extends g(p)≥0 to p=0,1.

4.1step 1.1step 2.1step 3.1algebra∎

Therefore D(P∥Q)≥2d2=12∥P−Q∥12. Since ∥v∥2≤∥v∥1 for every finite vector v, the Euclidean bound follows.

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Sources