We study the quality of weighted shortest paths when a continuous 2-dimensional space is discretized by a weighted triangular tessellation. In order to evaluate how well the tessellation approximates the 2-dimensional space, we study three types of shortest paths: a weighted shortest path SPw(s,t), which is a shortest path from s to t in the space; a weighted shortest vertex path SVPw(s,t), which is an any-angle shortest path; and a weighted shortest grid path SGPw(s,t), which is a shortest path whose edges are edges of the tessellation. Given any arbitrary weight assignment to the faces of a triangular tessellation, thus extending recent results by Bailey et al. (2021) [6], we prove upper and lower bounds on the ratios ‖SGPw(s,t)‖‖SPw(s,t)‖, ‖SVPw(s,t)‖‖SPw(s,t)‖, ‖SGPw(s,t)‖‖SVPw(s,t)‖, which provide estimates on the quality of the approximation. It turns out, surprisingly, that our worst-case bounds are independent of any weight assignment. Our main result is that ‖SGPw(s,t)‖‖SPw(s,t)‖=23≈1.15 in the worst case, and this is tight. As a corollary, for the weighted any-angle path SVPw(s,t) we obtain the approximation result ‖SVPw(s,t)‖‖SPw(s,t)‖⪅1.15.

Abstract We explore the separability of point sets in the plane by a restricted-orientation convex hull, which is an orientation-dependent, possibly disconnected, and non-convex enclosing shape that generalizes the convex hull. Let R and B be two disjoint sets of red and blue points in the plane, and $$\mathcal {O}$$ O be a set of $$k\ge 2$$ k ≥ 2 lines passing through the origin. We study the problem of computing the set of orientations of the lines of $$\mathcal {O}$$ O for which the $$\mathcal {O}$$ O -convex hull of R contains no points of B. For $$k=2$$ k = 2 orthogonal lines we have the rectilinear convex hull. In optimal $$O(n\log n)$$ O ( n log n ) time and O(n) space, $$n = \vert R \vert + \vert B \vert $$ n = | R | + | B | , we compute the set of rotation angles such that, after simultaneously rotating the lines of $$\mathcal {O}$$ O around the origin in the same direction, the rectilinear convex hull of R contains no points of B. We generalize this result to the case where $$\mathcal {O}$$ O is formed by $$k \ge 2$$ k ≥ 2 lines with arbitrary orientations. In the counter-clockwise circular order of the lines of $$\mathcal {O}$$ O , let $$\alpha _i$$ α i be the angle required to clockwise rotate the ith line so it coincides with its successor. We solve the problem in this case in $$O({1}/{\Theta }\cdot N \log N)$$ O ( 1 / Θ · N log N ) time and $$O({1}/{\Theta }\cdot N)$$ O ( 1 / Θ · N ) space, where $$\Theta = \min \{ \alpha _1,\ldots ,\alpha _k \}$$ Θ = min { α 1 , … , α k } and $$N=\max \{k,\vert R \vert + \vert B \vert \}$$ N = max { k , | R | + | B | } . We finally consider the case in which $$\mathcal {O}$$ O is formed by $$k=2$$ k = 2 lines, one of the lines is fixed, and the second line rotates by an angle that goes from 0 to $$\pi $$ π . We show that this last case can also be solved in optimal $$O(n\log n)$$ O ( n

Given a set S of regions with piece-wise linear boundary and a positive angle α < 90°, we consider the problem of computing the locations and orientations of the minimum number of α-floodlights positioned at points in S which suffice to illuminate the entire x-axis. We show that the problem can be solved in O(n log n) time and O(n) space, where n is the number of vertices of the set S.

Let $P$łabelpage1 be a set of $n$ points in the plane. We consider a variation of the classical Erdös--Szekeres problem, presenting efficient algorithms with $O(n^3)$ running time and $O(n^2)$ space complexity that compute (1) a subset $S$ of $P$ such that the boundary of the rectilinear convex hull of $S$ has the maximum number of points from $P$, (2) a subset $S$ of $P$ such that the boundary of the rectilinear convex hull of $S$ has the maximum number of points from $P$ and its interior contains no element of $P$, (3) a subset $S$ of $P$ such that the rectilinear convex hull of $S$ has maximum area and its interior contains no element of $P$, and (4) when each point of $P$ is assigned a weight, positive or negative, a subset $S$ of $P$ that maximizes the total weight of the points in the rectilinear convex hull of $S$. We also revisit the problems of computing a maximum area orthoconvex polygon and computing a maximum area staircase polygon, amidst a point set in a rectangular domain. We obtain new and simpler algorithms to solve both problems with the same complexity as in the state of the art.

Due to the high density of Wi‐Fi networks, especially in the unlicensed 2.4 GHz frequency band, channel assignment has become a critical duty for achieving a satisfactory user experience. Probably, the main peculiarity of Wi‐Fi networks is the partial overlap of the radio channels that can be used by access points. For that reason, a number of works avoid cochannel interferences by using only channels which are far enough from each other to have no interferences, the so‐called orthogonal channels. However, there is a range of choices between using the whole spectrum and using only orthogonal channels. In this work we evaluate the influence of the choice of channel set in realistic settings, using both optimization and heuristic approaches. Results show that the optimizer is not able to achieve better results when using the whole spectrum instead of restricting to only the orthogonal channels. In fact, the optimizer uses mainly the orthogonal channels when they are available, while the heuristics considered lose performance when more channels are available. We believe this insight will be useful to design new heuristics for Wi‐Fi channel assignment.

We prove that for every centrally symmetric convex polygon Q, there exists a constant α such that any αk-fold covering of the plane by translates of Q can be decomposed into k coverings. This improves on a quadratic upper bound proved by Pach and Tóth (SoCG'07). The question is motivated by a sensor network problem, in which a region has to be monitored by sensors with limited battery life.

Scheduling for speculative parallelization is a problem that remained unsolved despite its importance. Simple methods such as Fixed-Size Chunking (FSC) need several 'dry-runs' before an acceptable chunk size is found. Other traditional scheduling methods were originally designed for loops with no dependences, so they are primarily focused in the problem of load balancing. In general, all these methods perform poorly when used for speculative parallelization, where loops may present unexpected dependences that adversely affect performance. In this work we address the problem of scheduling loops with and without dependences for speculative execution. We have found that a trade-off between minimizing the number of re-executions and reducing overheads can be found if the size of the scheduled block of iterations is calculated at runtime. We introduce here a scheduling method called Just-In- Time (JIT) scheduling that uses the information available during the execution of the loop in order to dynamically compute the size of the next block to be scheduled. The results show a 10% to 26% speedup improvement in real applications with dependences with respect to a carefully- tuned FSC strategy, and a 9% to 39% speedup improvement in real applications without dependences. With our proposal, the number of dependence violations that lead to squashes can be reduced by up to 62%. Moreover, in applications where the cost of dependence violations is too high to obtain speedups with FSC, our runtime scheduling mechanism avoids performance degradation.

In this work, we address the problem of scheduling loops with dependences in the context of speculative parallelization. We show that the scheduling alternatives are highly influenced by the dependence violation pattern the code presents. We center our analysis in those algorithms where dependences are less likely to appear as the execution proceeds. Particularly, we focus on randomized incremental algorithms, widely used as a much more efficient solution to many problems than their deterministic counterparts. These important algorithms are, in general, hard to parallelize by hand and represent a challenge for any automatic parallelization scheme. Our analysis led us to the development of MESETA, a new scheduling strategy that takes into account the probability of a dependence violation to determine the number of iterations being scheduled. MESETA is compared with existing techniques, including fixed-size chunking (FSC), the only scheduling alternative used so far in the context of speculative parallelization. Our experimental results show a 5.5 percent to 36.25 percent speedup improvement over FSC, leading to a better extraction of the parallelism inherent to randomized incremental algorithms. Moreover, when the cost of dependence violations is too high to obtain speedups, MESETA curves the performance degradation

For any finite set A of n points in general position in R2, we define a (3n-3)-dimensional simple polyhedron whose face poset is isomorphic to the poset of "non-crossing marked graphs" with vertex set A, where a marked graph is defined as a geometric graph together with a subset of its pointed vertices. The poset of non-crossing graphs on A appears as the complement of the star of a face in that polyhedron.The polyhedron has a unique maximal bounded face, of dimension 3n-3-2n;b; where n;b; is the number of convex hull points of A. The vertices of this polytope are all the pseudo triangulations of A, and the edges are flips of two types: the traditional diagonal flips (in pseudo-triangulations) and the removal or insertion of a single edge.

Pointed pseudo-triangulations are planar minimally rigid graphs embedded in the plane with pointed vertices (incident to an angle larger than p). In this paper we prove that the opposite statement is also true, namely that planar minimally rigid graphs always admit pointed embeddings, even under certain natural topological and combinatorial constraints. The proofs yield efficient embedding algorithms. They also provide---to the best of our knowledge---the first algorithmically effective result on graph embeddings with oriented matroid constraints other than convexity of faces.