Geometric Dominating Set and Set Cover via Local Search

In this paper, we study two classic optimization problems: minimum geometric dominating set and set cover. Both the problems have been studied for different types of objects for a long time. These problems become APX-hard when the objects are axis-parallel rectangles, ellipses, $\alpha$-fat objects of constant description complexity, and convex polygons. On the other hand, PTAS (polynomial time approximation scheme) is known for them when the objects are disks or unit squares. Surprisingly, PTAS was unknown even for arbitrary squares. For homothetic set of convex objects, an $O(k^4)$ approximation algorithm is known for dominating set problem, where $k$ is the number of corners in a convex object. On the other hand, QPTAS (quasi polynomial time approximation scheme) is known very recently for the covering problem when the objects are pseudodisks. For both problems obtaining a PTAS remains open for a large class of objects. For the dominating set problems, we prove that the popular local search algorithm leads to an $(1+\varepsilon)$ approximation when objects are homothetic set of convex objects (which includes arbitrary squares, $k$-regular polygons, translated and scaled copies of a convex set etc.) in $n^{O(1/\varepsilon^2)}$ time. On the other hand, the same technique leads to a PTAS for geometric covering problem when the objects are convex pseudodisks (which includes disks, unit height rectangles, homothetic convex objects etc.). As a consequence, we obtain an easy to implement approximation algorithm for both problems for a large class of objects, significantly improving the best known approximation guarantees.Comment: 25 pages, 4 figure

Algorithms for Intersection Graphs of Multiple Intervals and Pseudo Disks

Intersection graphs of planar geometric objects such as intervals, disks, rectangles and pseudo-disks are well studied. Motivated by various applications, Butman et al. in SODA 2007 considered algorithmic questions in intersection graphs of $t$-intervals. A $t$-interval is a union of at most $t$ distinct intervals (here $t$ is a parameter) -- these graphs are referred to as Multiple-Interval Graphs. Subsequent work by Kammer et al. in Approx 2010 also considered $t$-disks and other geometric shapes. In this paper we revisit some of these algorithmic questions via more recent developments in computational geometry. For the minimum weight dominating set problem, we give a simple $O(t \log t)$ approximation for Multiple-Interval Graphs, improving on the previously known bound of $t^2$ . We also show that it is NP-hard to obtain an $o(t)$-approximation in this case. In fact, our results hold for the intersection graph of a set of t-pseudo-disks which is a much larger class. We obtain an ${\Omega}(1/t)$-approximation for the maximum weight independent set in the intersection graph of $t$-pseudo-disks. Our results are based on simple reductions to existing algorithms by appropriately bounding the union complexity of the objects under consideration

The Robust Minimal Controllability Problem

In this paper, we address two minimal controllability problems, where the goal is to determine a minimal subset of state variables in a linear time-invariant system to be actuated to ensure controllability under additional constraints. First, we study the problem of characterizing the sparsest input matrices that assure controllability when the autonomous dynamics' matrix is simple. Secondly, we build upon these results to describe the solutions to the robust minimal controllability problem, where the goal is to determine the sparsest input matrix ensuring controllability when specified number of inputs fail. Both problems are NP-hard, but under the assumption that the dynamics' matrix is simple, we show that it is possible to reduce these two problems to set multi-covering problems. Consequently, these problems share the same computational complexity, i.e., they are NP-complete, but polynomial algorithms to approximate the solutions of a set multi-covering problem can be leveraged to obtain close-to-optimal solutions to either of the minimal controllability problems