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The closed convex perspective is subadditive
Statement
Let be a convex generator from Finite f-divergence with support conventions. Define its closed perspective on by For every finite family ,
Facts & Assumptions
Given: A convex generator with the extended boundary values and defined in Finite f-divergence with support conventions, and a finite family of nonnegative pairs .
Finite f-divergence with support conventions: is convex on , , , and ; the value at a positive first coordinate and zero second coordinate is the corresponding first coordinate times .
Finite Jensen inequality for convex functions on : for a finite family of points in a convex domain and nonnegative weights summing to one, the convex function at their weighted average is at most the weighted sum of its values.
Boundary limits of a normalized finite convex generator exist: every finite convex generator is continuous at every positive argument.
Proof
Put and . If , all . If , every pair is and both sides are zero. If , both sides equal by the definition of , including the convention that a positive coefficient times is .
Suppose and no index has . If any with , the right side is and the inequality holds. If is finite, extend to the convex domain by that value. This extension is convex at the endpoint too: approximate a zero argument in any convexity inequality by positive arguments and pass to the limit using from [F1] and continuity at positive arguments from [F3]. If , the excluded case shows that every index with has , so all ratios used below lie in the original convex domain . For indices with , the weights sum to one and . Applying [F2] gives ; terms contribute zero. This is the asserted inequality.
Suppose and is nonempty. If , the right side is , so the inequality holds. If any with , the right side is and the inequality holds. Otherwise, if is finite, replace each with by , leaving all other second coordinates unchanged. the finite Jensen inequality [F2] then gives . As , the left side tends to by [F3] at the positive number , and each last summand tends to by the definition of . The resulting inequality is exactly .
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Used by
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Sources
- Yury Polyanskiy and Yihong Wu, Information Theory: From Coding to Learning, Chapter 7, §7.1 (standard reference, not scraped)
- Igal Sason and Sergio Verdú, f-Divergence Inequalities, §II-B, equations (35)–(39) (standard reference, not scraped)