No Arabic abstract
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 $k$ vertices 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.
Given an initial placement of a set of rectangles in the plane, we consider the problem of finding a disjoint placement of the rectangles that minimizes the area of the bounding box and preserves the orthogonal order i.e. maintains the sorted ordering of the rectangle centers along both $x$-axis and $y$-axis with respect to the initial placement. This problem is known as Layout Adjustment for Disjoint Rectangles(LADR). It was known that LADR is $mathbb{NP}$-hard, but only heuristics were known for it. We show that a certain decision version of LADR is $mathbb{APX}$-hard, and give a constant factor approximation for LADR.
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.
Given a graph $G=(V,E)$, the dominating set problem asks for a minimum subset of vertices $Dsubseteq V$ such that every vertex $uin Vsetminus D$ is adjacent to at least one vertex $vin D$. That is, the set $D$ satisfies the condition that $|N[v]cap D|geq 1$ for each $vin V$, where $N[v]$ is the closed neighborhood of $v$. In this paper, we study two variants of the classical dominating set problem: $boldmath{k}$-tuple dominating set ($k$-DS) problem and Liars dominating set (LDS) problem, and obtain several algorithmic and hardness results. On the algorithmic side, we present a constant factor ($frac{11}{2}$)-approximation algorithm for the Liars dominating set problem on unit disk graphs. Then, we obtain a PTAS for the $boldmath{k}$-tuple dominating set problem on unit disk graphs. On the hardness side, we show a $Omega (n^2)$ bits lower bound for the space complexity of any (randomized) streaming algorithm for Liars dominating set problem as well as for the $boldmath{k}$-tuple dominating set problem. Furthermore, we prove that the Liars dominating set problem on bipartite graphs is W[2]-hard.
A fixed-mobile bigraph G is a bipartite graph such that the vertices of one partition set are given with fixed positions in the plane and the mobile vertices of the other part, together with the edges, must be added to the drawing. We assume that G is planar and study the problem of finding, for a given k >= 0, a planar poly-line drawing of G with at most k bends per edge. In the most general case, we show NP-hardness. For k=0 and under additional constraints on the positions of the fixed or mobile vertices, we either prove that the problem is polynomial-time solvable or prove that it belongs to NP. Finally, we present a polynomial-time testing algorithm for a certain type of layered 1-bend drawings.
We consider a spectrum of geometric optimization problems motivated by contexts such as satellite communication and astrophysics. In the problem Minimum Scan Cover with Angular Costs, we are given a graph $G$ that is embedded in Euclidean space. The edges of $G$ need to be scanned, i.e., probed from both of their vertices. In order to scan their edge, two vertices need to face each other; changing the heading of a vertex incurs some cost in terms of energy or rotation time that is proportional to the corresponding rotation angle. Our goal is to compute schedules that minimize the following objective functions: (i) in Minimum Makespan Scan Cover (MSC-MS), this is the time until all edges are scanned; (ii) in Minimum Total Energy Scan Cover (MSC-TE), the sum of all rotation angles; (iii) in Minimum Bottleneck Energy Scan Cover (MSC-BE), the maximum total rotation angle at one vertex. Previous theoretical work on MSC-MS revealed a close connection to graph coloring and the cut cover problem, leading to hardness and approximability results. In this paper, we present polynomial-time algorithms for 1D instances of MSC-TE and MSC-BE, but NP-hardness proofs for bipartite 2D instances. For bipartite graphs in 2D, we also give 2-approximation algorithms for both MSC-TE and MSC-BE. Most importantly, we provide a comprehensive study of practical methods for all three problems. We compare three different mixed-integer programming and two constraint programming approaches, and show how to compute provably optimal solutions for geometric instances with up to 300 edges. Additionally, we compare the performance of different meta-heuristics for even larger instances.