Estimating Smooth and Convex Functions

Optimization of Smooth and Strongly Convex Functions

A. Proof of Lemma 1 We need the following lemma that characterizes the property of the extra-gradient descent. Lemma 8 (Lemma 3.1 in (Nemirovski, 2005)). Let Z be a convex compact set in Euclidean space E with inner product 〈·, ·〉, let ‖ · ‖ be a norm on E and ‖ · ‖∗ be its dual norm, and let ω(z) : Z 7→ R be a α-strongly convex function with respect to ‖ · ‖. The Bregman distance associated wi...

Smooth Convex Bodies with Proportional Projection Functions

For a convex body K ⊂ Rn and i ∈ {1, . . . , n− 1}, the function assigning to any i-dimensional subspace L of Rn, the i-dimensional volume of the orthogonal projection of K to L, is called the i-th projection function of K. Let K, K0 ⊂ Rn be smooth convex bodies of class C2 +, and let K0 be centrally symmetric. Excluding two exceptional cases, we prove that K and K0 are homothetic if they have ...

On Non-smooth Convex Distance Functions

L-convex Functions and M-convex Functions

In the field of nonlinear programming (in continuous variables) convex analysis [22, 23] plays a pivotal role both in theory and in practice. An analogous theory for discrete optimization (nonlinear integer programming), called " discrete convex analysis " [18, 17], is developed for L-convex and M-convex functions by adapting the ideas in convex analysis and generalizing the results in matroid ...

Extremal Approximately Convex Functions and Estimating the Size of Convex Hulls

A real valued function f defined on a convex K is an approximately convex function iff it satisfies