10 research outputs found

    Sources of Superlinearity in Davenport-Schinzel Sequences

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    A generalized Davenport-Schinzel sequence is one over a finite alphabet that contains no subsequences isomorphic to a fixed forbidden subsequence. One of the fundamental problems in this area is bounding (asymptotically) the maximum length of such sequences. Following Klazar, let Ex(\sigma,n) be the maximum length of a sequence over an alphabet of size n avoiding subsequences isomorphic to \sigma. It has been proved that for every \sigma, Ex(\sigma,n) is either linear or very close to linear; in particular it is O(n 2^{\alpha(n)^{O(1)}}), where \alpha is the inverse-Ackermann function and O(1) depends on \sigma. However, very little is known about the properties of \sigma that induce superlinearity of \Ex(\sigma,n). In this paper we exhibit an infinite family of independent superlinear forbidden subsequences. To be specific, we show that there are 17 prototypical superlinear forbidden subsequences, some of which can be made arbitrarily long through a simple padding operation. Perhaps the most novel part of our constructions is a new succinct code for representing superlinear forbidden subsequences

    Davenport-Schinzel theory of matrices

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    AbstractLet C be a configuration of 1's. We define f(n;C) to be the maximal number of 1's in a 0–1 matrix of size n × n not having C as a subconfiguration. We consider the problem of determining the order of f(n;C) for several forbidden C's. Among other results we prove that f(n;1111) = Θ(α(n)n), where α(n) is the inverse of the Ackermann function

    Between umbra and penumbra

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    International audienceComputing shadow boundaries is a difficult problem in the case of non-point light sources. A point is in the umbra if it does not see any part of any light source; it is in full light if it sees entirely all the light sources; otherwise, it is in the penumbra. While the common boundary of the penumbra and the full light is well understood, less is known about the boundary of the umbra. In this paper we prove various bounds on the complexity of the umbra and the penumbra cast by a segment or polygonal light source on a plane in the presence of polygon or polytope obstacles. In particular, we show that a single segment light source may cast on a plane, in the presence of two triangles, four connected components of umbra and that two fat convex obstacles of total complexity n can engender Omega(n) connected components of umbra. In a scene consisting of a segment light source and k disjoint polytopes of total complexity n, we prove an Omega(nk^2+k^4) lower bound on the maximum number of connected components of the umbra and a O(nk^3) upper bound on its complexity. We also prove that, in the presence of k disjoint polytopes of total complexity n, some of which being light sources, the umbra cast on a plane may have Omega(n^2k^3 + nk^5) connected components and has complexity O(n^3k^3). These are the first bounds on the size of the umbra in terms of both k and n. These results prove that the umbra, which is bounded by arcs of conics, is intrinsically much more intricate than the full light/penumbra boundary which is bounded by line segments and whose worst-case complexity is in Omega(n alpha(k) +km +k^2) and O(n alpha(k) +km alpha(k) +k^2), where m is the complexity of the polygonal light source

    Improved bounds and new techniques for Davenport-Schinzel sequences and their generalizations

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    Let lambda_s(n) denote the maximum length of a Davenport-Schinzel sequence of order s on n symbols. For s=3 it is known that lambda_3(n) = Theta(n alpha(n)) (Hart and Sharir, 1986). For general s>=4 there are almost-tight upper and lower bounds, both of the form n * 2^poly(alpha(n)) (Agarwal, Sharir, and Shor, 1989). Our first result is an improvement of the upper-bound technique of Agarwal et al. We obtain improved upper bounds for s>=6, which are tight for even s up to lower-order terms in the exponent. More importantly, we also present a new technique for deriving upper bounds for lambda_s(n). With this new technique we: (1) re-derive the upper bound of lambda_3(n) <= 2n alpha(n) + O(n sqrt alpha(n)) (first shown by Klazar, 1999); (2) re-derive our own new upper bounds for general s; and (3) obtain improved upper bounds for the generalized Davenport-Schinzel sequences considered by Adamec, Klazar, and Valtr (1992). Regarding lower bounds, we show that lambda_3(n) >= 2n alpha(n) - O(n), and therefore, the coefficient 2 is tight. We also present a simpler version of the construction of Agarwal, Sharir, and Shor that achieves the known lower bounds for even s>=4.Comment: To appear in Journal of the ACM. 48 pages, 3 figure

    Sharp Bounds on Davenport-Schinzel Sequences of Every Order

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    One of the longest-standing open problems in computational geometry is to bound the lower envelope of nn univariate functions, each pair of which crosses at most ss times, for some fixed ss. This problem is known to be equivalent to bounding the length of an order-ss Davenport-Schinzel sequence, namely a sequence over an nn-letter alphabet that avoids alternating subsequences of the form a⋯b⋯a⋯b⋯a \cdots b \cdots a \cdots b \cdots with length s+2s+2. These sequences were introduced by Davenport and Schinzel in 1965 to model a certain problem in differential equations and have since been applied to bounding the running times of geometric algorithms, data structures, and the combinatorial complexity of geometric arrangements. Let λs(n)\lambda_s(n) be the maximum length of an order-ss DS sequence over nn letters. What is λs\lambda_s asymptotically? This question has been answered satisfactorily (by Hart and Sharir, Agarwal, Sharir, and Shor, Klazar, and Nivasch) when ss is even or s≤3s\le 3. However, since the work of Agarwal, Sharir, and Shor in the mid-1980s there has been a persistent gap in our understanding of the odd orders. In this work we effectively close the problem by establishing sharp bounds on Davenport-Schinzel sequences of every order ss. Our results reveal that, contrary to one's intuition, λs(n)\lambda_s(n) behaves essentially like λs−1(n)\lambda_{s-1}(n) when ss is odd. This refutes conjectures due to Alon et al. (2008) and Nivasch (2010).Comment: A 10-page extended abstract will appear in the Proceedings of the Symposium on Computational Geometry, 201

    Vertical Decompositions for Triangles in 3-Space

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    Introduction. Springer-Verlag, New York, NY, 1985. [33] F. P. Preparata and R. Tamassia. Efficient point location in a convex spatial cell-complex. SIAM J. Comput., 21:267--280, 1992. [34] M. Sharir. Almost tight upper bounds for lower envelopes in higher dimensions. In Proc. 34th Annu. IEEE Sympos. Found. Comput. Sci. (FOCS 93), pages 498-- 507, 1993. [35] R. E. Tarjan. Data Structures and Network Algorithms. Society for Industrial and Applied Mathematics, Philadelphia, PA, 1987. [36] A. Wiernik and M. Sharir. Planar realizations of nonlinear Davenport-Schinzel sequences by segments. Discrete Comput. Geom., 3:15--47, 1988. [15] K. L. Clarkson. A randomized algorithm for closest-point queries. SIAM J. Comput., 17:830--847, 1988. [16] K. L. Clarkson and P. W. Shor. Applications of random sampling in computational geometry, II. Discrete Comput. Geom., 4:387--421, 1989. [17] M. de Berg.&lt;F38
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