We show that the decision problem of determining whether a given (abstract simplicial) $k$-complex has a geometric embedding in $mathbb R^d$ is complete for the Existential Theory of the Reals for all $dgeq 3$ and $kin{d-1,d}$. This implies that the
problem is polynomial time equivalent to determining whether a polynomial equation system has a real root. Moreover, this implies NP-hardness and constitutes the first hardness results for the algorithmic problem of geometric embedding (abstract simplicial) complexes.
Given a neural network, training data, and a threshold, it was known that it is NP-hard to find weights for the neural network such that the total error is below the threshold. We determine the algorithmic complexity of this fundamental problem preci
sely, by showing that it is ER-complete. This means that the problem is equivalent, up to polynomial-time reductions, to deciding whether a system of polynomial equations and inequalities with integer coefficients and real unknowns has a solution. If, as widely expected, ER is strictly larger than NP, our work implies that the problem of training neural networks is not even in NP.
Given two shapes $A$ and $B$ in the plane with Hausdorff distance $1$, is there a shape $S$ with Hausdorff distance $1/2$ to and from $A$ and $B$? The answer is always yes, and depending on convexity of $A$ and/or $B$, $S$ may be convex, connected, o
r disconnected. We show that our result can be generalised to give an interpolated shape between $A$ and $B$ for any interpolation variable $alpha$ between $0$ and $1$, and prove that the resulting morph has a bounded rate of change with respect to $alpha$. Finally, we explore a generalization of the concept of a Hausdorff middle to more than two input sets. We show how to approximate or compute this middle shape, and that the properties relating to the connectedness of the Hausdorff middle extend from the case with two input sets. We also give bounds on the Hausdorff distance between the middle set and the input.
We show that many natural two-dimensional packing problems are algorithmically equivalent to finding real roots of multivariate polynomials. A two-dimensional packing problem is defined by the type of pieces, containers, and motions that are allowed.
The aim is to decide if a given set of pieces can be placed inside a given container. The pieces must be placed so that they are pairwise interior-disjoint, and only motions of the allowed type can be used to move them there. We establish a framework which enables us to show that for many combinations of allowed pieces, containers, and motions, the resulting problem is $existsmathbb R$-complete. This means that the problem is equivalent (under polynomial time reductions) to deciding whether a given system of polynomial equations and inequalities with integer coefficients has a real solution. We consider packing problems where only translations are allowed as the motions, and problems where arbitrary rigid motions are allowed, i.e., both translations and rotations. When rotations are allowed, we show that the following combinations of allowed pieces and containers are $existsmathbb R$-complete: $bullet$ simple polygons, each of which has at most 8 corners, into a square, $bullet$ convex objects bounded by line segments and hyperbolic curves into a square, $bullet$ convex polygons into a container bounded by line segments and hyperbolic curves. Restricted to translations, we show that the following combinations are $existsmathbb R$-complete: $bullet$ objects bounded by segments and hyperbolic curves into a square, $bullet$ convex polygons into a container bounded by segments and hyperbolic curves.
We study the complexity of Maximum Clique in intersection graphs of convex objects in the plane. On the algorithmic side, we extend the polynomial-time algorithm for unit disks [Clark 90, Raghavan and Spinrad 03] to translates of any fixed convex set
. We also generalize the efficient polynomial-time approximation scheme (EPTAS) and subexponential algorithm for disks [Bonnet et al. 18, Bonamy et al. 18] to homothets of a fixed centrally symmetric convex set. The main open question on that topic is the complexity of Maximum Clique in disk graphs. It is not known whether this problem is NP-hard. We observe that, so far, all the hardness proofs for Maximum Clique in intersection graph classes $mathcal I$ follow the same road. They show that, for every graph $G$ of a large-enough class $mathcal C$, the complement of an even subdivision of $G$ belongs to the intersection class $mathcal I$. Then they conclude invoking the hardness of Maximum Independent Set on the class $mathcal C$, and the fact that the even subdivision preserves that hardness. However there is a strong evidence that this approach cannot work for disk graphs [Bonnet et al. 18]. We suggest a new approach, based on a problem that we dub Max Interval Permutation Avoidance, which we prove unlikely to have a subexponential-time approximation scheme. We transfer that hardness to Maximum Clique in intersection graphs of objects which can be either half-planes (or unit disks) or axis-parallel rectangles. That problem is not amenable to the previous approach. We hope that a scaled down (merely NP-hard) variant of Max Interval Permutation Avoidance could help making progress on the disk case, for instance by showing the NP-hardness for (convex) pseudo-disks.
Recently, various natural algorithmic problems have been shown to be $exists mathbb{R}$-complete. The reduction relied in many cases on the $exists mathbb{R}$-completeness of the problem ETR-INV, which served as a useful intermediate problem. Often s
ome strengthening and modification of ETR-INV was required. This lead to a cluttered situation where no paper included all the previous details. Here, we give a streamlined exposition in a self-contained manner. We also explain and prove various universality results regarding ETR-INV. These notes should not be seen as a research paper with new results. However, we do describe some refinements of earlier results which might be useful for future research. We plan to extend and update this exposition as seems fit.
In a nutshell, we show that polynomials and nested polytopes are topological, algebraic and algorithmically equivalent. Given two polytops $Asubseteq B$ and a number $k$, the Nested Polytope Problem (NPP) asks, if there exists a polytope $X$ on $k$ v
ertices such that $Asubseteq X subseteq B$. The polytope $A$ is given by a set of vertices and the polytope $B$ is given by the defining hyperplanes. We show a universality theorem for NPP. Given an instance $I$ of the NPP, we define the solutions set of $I$ as $$ V(I) = {(x_1,ldots,x_k)in mathbb{R}^{kcdot n} : Asubseteq text{conv}(x_1,ldots,x_k) subseteq B}.$$ As there are many symmetries, induced by permutations of the vertices, we will consider the emph{normalized} solution space $V(I)$. Let $F$ be a finite set of polynomials, with bounded solution space. Then there is an instance $I$ of the NPP, which has a rationally-equivalent normalized solution space $V(I)$. Two sets $V$ and $W$ are rationally equivalent if there exists a homeomorphism $f : V rightarrow W$ such that both $f$ and $f^{-1}$ are given by rational functions. A function $f:Vrightarrow W$ is a homeomorphism, if it is continuous, invertible and its inverse is continuous as well. As a corollary, we show that NPP is $exists mathbb{R}$-complete. This implies that unless $exists mathbb{R} =$ NP, the NPP is not contained in the complexity class NP. Note that those results already follow from a recent paper by Shitov. Our proof is geometric and arguably easier.
In the Art Gallery Problem we are given a polygon $Psubset [0,L]^2$ on $n$ vertices and a number $k$. We want to find a guard set $G$ of size $k$, such that each point in $P$ is seen by a guard in $G$. Formally, a guard $g$ sees a point $p in P$ if t
he line segment $pg$ is fully contained inside the polygon $P$. The history and practical findings indicate that irrational coordinates are a very rare phenomenon. We give a theoretical explanation. Next to worst case analysis, Smoothed Analysis gained popularity to explain the practical performance of algorithms, even if they perform badly in the worst case. The idea is to study the expected performance on small perturbations of the worst input. The performance is measured in terms of the magnitude $delta$ of the perturbation and the input size. We consider four different models of perturbation. We show that the expected number of bits to describe optimal guard positions per guard is logarithmic in the input and the magnitude of the perturbation. This shows from a theoretical perspective that rational guards with small bit-complexity are typical. Note that describing the guard position is the bottleneck to show NP-membership. The significance of our results is that algebraic methods are not needed to solve the Art Gallery Problem in typical instances. This is the first time an $existsmathbb{R}$-complete problem was analyzed by Smoothed Analysis.
