For each fixed , choose the convex perturbation function
It is jointly convex and has marginal
Because and are nonnegative and finite everywhere,
for every . The inf-convolution is convex: for approximate decompositions of , take their convex combination and use convexity of both costs; then let their approximation errors tend to zero. Thus is a finite convex function on all of , hence continuous everywhere. In particular is proper and finite near zero, so the qualification in the previous solution applies at every :
This is strong duality for a finite-valued infimal convolution.
No primal attainment was used. For example and meet all the stated assumptions, but is approached only as . This illustrates why a proof based on an assumed minimizing decomposition would be incomplete. The printed functions are real-valued everywhere, not merely extended-real; that distinction supplies continuity and the strong-duality qualification.

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