It is well known that a strictly convex minimand admits at most one minimizer. We prove a partial converse: Let XX be a locally convex Hausdorff space and f ⁣:X(,]f\colon X\to (-\infty, \infty] a function with compact sublevel sets and exhibiting some mildly superlinear growth. Then each tilted minimization problem
[2mm] \centerline{minxXf(x)x,xX\displaystyle \min_{x \in X} f(x) - \langle x', x \rangle_X}
[-2mm] admits at most one minimizer as xx' ranges over dom(f)\text{\rm dom}\, \left( \partial f^* \right) if and only if the biconjugate ff^{**} is essentially strictly convex and agrees with ff at all points where ff^{**} is subdifferentiable. We prove this via a representation formula for ff^{**} that might be of independent interest.

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T. Ruf, B. Schmidt. “Unique Minimizers and the Representation of Convex Envelopes in Locally Convex Vector Spaces.” Journal of Convex Analysis 29 (2022), No. 3, 929–937.