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We construct norming meshes for polynomial optimization by the classical Markov inequality on general convex bodies in $${\mathbb {R}}^d$$ R d , and by a tangential Markov inequality via an estimate of the Dubiner distance on smooth convex bodies. These allow to compute a $$(1-\varepsilon )$$ ( 1 - ε ) -approximation to the minimum of any polynomial of degree not exceeding n by $${\mathcal...
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