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Containment problems belong to the classical problems of (convex) geometry. In the proper sense, a containment problem is the task to decide the set-theoretic inclusion of two given sets, which is hard from both the theoretical and the practical perspective. In a broader sense, this includes, e.g., radii or packing problems, which are even harder. For some classes of convex sets there has been strong interest in containment problems. This includes containment problems of polyhedra and balls, and containment of polyhedra, which have been studied in the late 20th century because of their inherent relevance in linear programming and combinatorics.
Since then, there has only been limited progress in understanding containment problems of that type. In recent years, containment problems for spectrahedra, which naturally generalize the class of polyhedra, have seen great interest. This interest is particularly driven by the intrinsic relevance of spectrahedra and their projections in polynomial optimization and convex algebraic geometry. Except for the treatment of special classes or situations, there has been no overall treatment of that kind of problems, though.
In this thesis, we provide a comprehensive treatment of containment problems concerning polyhedra, spectrahedra, and their projections from the viewpoint of low-degree semialgebraic problems and study algebraic certificates for containment. This leads to a new and systematic access to studying containment problems of (projections of) polyhedra and spectrahedra, and provides several new and partially unexpected results.
The main idea - which is meanwhile common in polynomial optimization, but whose understanding of the particular potential on low-degree geometric problems is still a major challenge - can be explained as follows. One point of view towards linear programming is as an application of Farkas' Lemma which characterizes the (non-)solvability of a system of linear inequalities. The affine form of Farkas' Lemma characterizes linear polynomials which are nonnegative on a given polyhedron. By omitting the linearity condition, one gets a polynomial nonnegativity question on a semialgebraic set, leading to so-called Positivstellensaetze (or, more precisely Nichtnegativstellensaetze). A Positivstellensatz provides a certificate for the positivity of a polynomial function in terms of a polynomial identity. As in the linear case, these Positivstellensaetze are the foundation of polynomial optimization and relaxation methods. The transition from positivity to nonnegativity is still a major challenge in real algebraic geometry and polynomial optimization.
With this in mind, several principal questions arise in the context of containment problems: Can the particular containment problem be formulated as a polynomial nonnegativity (or, feasibility) problem in a sophisticated way? If so, how are positivity and nonnegativity related to the containment question in the sense of their geometric meaning? Is there a sophisticated Positivstellensatz for the particular situation, yielding certificates for containment? Concerning the degree of the semialgebraic certificates, which degree is necessary, which degree is sufficient to decide containment?
Indeed, (almost) all containment problems studied in this thesis can be formulated as polynomial nonnegativity problems allowing the application of semialgebraic relaxations. Other than this general result, the answer to all the other questions (highly) depends on the specific containment problem, particularly with regard to its underlying geometry. An important point is whether the hierarchies coming from increasing the degree in the polynomial relaxations always decide containment in finitely many steps.
We focus on the containment problem of an H-polytope in a V-polytope and of a spectrahedron in a spectrahedron. Moreover, we address containment problems concerning projections of H-polyhedra and spectrahedra. This selection is justified by the fact that the mentioned containment problems are computationally hard and their geometry is not well understood.

We study continuous dually epi-translation invariant valuations on certain cones of convex functions containing the space of finite-valued convex functions. Using the homogeneous decomposition of this space, we associate a certain distribution to any homogeneous valuation similar to the Goodey-Weil embedding for translation invariant valuations on convex bodies. The support of these distributions induces a corresponding notion of support for the underlying valuations, which imposes certain restrictions on these functionals, and we study the relation between the support of a valuation and its domain. This gives a partial answer to the question which dually epi-translation invariant valuations on finite-valued convex functions can be extended to larger cones of convex functions.
We also study topological properties of spaces of valuations with support contained in a fixed compact set. As an application of these results, we introduce the class of smooth valuations on convex functions and show that the subspace of smooth dually epi-translation invariant valuations is dense in the space of continuous dually epi-translation invariant valuation on finite-valued convex functions. These smooth valuations are given by integrating certain smooth differential forms over the graph of the differential of a convex function. We use this construction to give a characterization of a dense subspace of all continuous valuations on finite-valued convex functions that are rotation invariant as well as dually epi-translation invariant.
Using results from Alesker's theory of smooth valuations on convex bodies, we also show that any smooth valuation can be written as a convergent sum of mixed Hessian valuations. In particular, mixed Hessian valuations span a dense subspace, which is a version of McMullen’s conjecture for valuations on convex functions.