54 research outputs found

    Approximate Counting of k-Paths: Deterministic and in Polynomial Space

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    A few years ago, Alon et al. [ISMB 2008] gave a simple randomized O((2e)^km epsilon^{-2})-time exponential-space algorithm to approximately compute the number of paths on k vertices in a graph G up to a multiplicative error of 1 +/- epsilon. Shortly afterwards, Alon and Gutner [IWPEC 2009, TALG 2010] gave a deterministic exponential-space algorithm with running time (2e)^{k+O(log^3k)}m log n whenever epsilon^{-1}=k^{O(1)}. Recently, Brand et al. [STOC 2018] provided a speed-up at the cost of reintroducing randomization. Specifically, they gave a randomized O(4^km epsilon^{-2})-time exponential-space algorithm. In this article, we revisit the algorithm by Alon and Gutner. We modify the foundation of their work, and with a novel twist, obtain the following results. - We present a deterministic 4^{k+O(sqrt{k}(log^2k+log^2 epsilon^{-1}))}m log n-time polynomial-space algorithm. This matches the running time of the best known deterministic polynomial-space algorithm for deciding whether a given graph G has a path on k vertices. - Additionally, we present a randomized 4^{k+O(log k(log k + log epsilon^{-1}))}m log n-time polynomial-space algorithm. While Brand et al. make non-trivial use of exterior algebra, our algorithm is very simple; we only make elementary use of the probabilistic method. Thus, the algorithm by Brand et al. runs in time 4^{k+o(k)}m whenever epsilon^{-1}=2^{o(k)}, while our deterministic and randomized algorithms run in time 4^{k+o(k)}m log n whenever epsilon^{-1}=2^{o(k^{1/4})} and epsilon^{-1}=2^{o(k/(log k))}, respectively. Prior to our work, no 2^{O(k)}n^{O(1)}-time polynomial-space algorithm was known. Additionally, our approach is embeddable in the classic framework of divide-and-color, hence it immediately extends to approximate counting of graphs of bounded treewidth; in comparison, Brand et al. note that their approach is limited to graphs of bounded pathwidth

    Algebraic Methods in the Congested Clique

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    In this work, we use algebraic methods for studying distance computation and subgraph detection tasks in the congested clique model. Specifically, we adapt parallel matrix multiplication implementations to the congested clique, obtaining an O(n1−2/ω)O(n^{1-2/\omega}) round matrix multiplication algorithm, where ω<2.3728639\omega < 2.3728639 is the exponent of matrix multiplication. In conjunction with known techniques from centralised algorithmics, this gives significant improvements over previous best upper bounds in the congested clique model. The highlight results include: -- triangle and 4-cycle counting in O(n0.158)O(n^{0.158}) rounds, improving upon the O(n1/3)O(n^{1/3}) triangle detection algorithm of Dolev et al. [DISC 2012], -- a (1+o(1))(1 + o(1))-approximation of all-pairs shortest paths in O(n0.158)O(n^{0.158}) rounds, improving upon the O~(n1/2)\tilde{O} (n^{1/2})-round (2+o(1))(2 + o(1))-approximation algorithm of Nanongkai [STOC 2014], and -- computing the girth in O(n0.158)O(n^{0.158}) rounds, which is the first non-trivial solution in this model. In addition, we present a novel constant-round combinatorial algorithm for detecting 4-cycles.Comment: This is work is a merger of arxiv:1412.2109 and arxiv:1412.266

    Graph and Hypergraph Decompositions for Exact Algorithms

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    This thesis studies exact exponential and fixed-parameter algorithms for hard graph and hypergraph problems. Specifically, we study two techniques that can be used in the development of such algorithms: (i) combinatorial decompositions of both the input instance and the solution, and (ii) evaluation of multilinear forms over semirings. In the first part of the thesis we develop new algorithms for graph and hypergraph problems based on techniques (i) and (ii). While these techniques are independently both useful, the work presented in this part is largely characterised by their joint application. That is, combining results from different pieces of the decompositions often takes the from of multilinear form evaluation task, and on the other hand, decompositions offer the basic structure for dynamic-programming-style algorithms for the evaluation of multilinear forms. As main positive results of the first part, we give algorithms for three different problem families. First, we give a fast evaluation algorithm for linear forms defined by a disjointness matrix of small sets. This can be applied to obtain faster algorithms for counting maximum-weight objects of small size, such as k-paths in graphs. Second, we give a general framework for exponential-time algorithms for finding maximum-weight subgraphs of bounded tree-width, based on the theory of tree decompositions. Besides basic combinatorial problems, this framework has applications in learning Bayesian network structures. Third, we give a fixed-parameter algorithm for finding unbalanced vertex cuts, that is, vertex cuts that separate a small number of vertices from the rest of the graph. In the second part of the thesis we consider aspects of the complexity theory of linear forms over semirings, in order to better understand technique (ii). Specifically, we study how the presence of different algebraic catalysts in the ground semiring affects the complexity. As the main result, we show that there are linear forms that are easy to compute over semirings with idempotent addition, but difficult to compute over rings, unless the strong exponential time hypothesis fails.Yksi tietojenkĂ€sittelytieteen perustavista tavoitteista on tehokkaiden algoritmien kehittĂ€minen. Teoreettisesta nĂ€kökulmasta algoritmia yleensĂ€ pidetÀÀn tehokkaana mikĂ€li sen ajoaika riippuu polynomisesti syötteen koosta. On kuitenkin laskennallisia ongelmia, joihin ei ole olemassa polynomiaikaisia algoritmeja. Esimerkiksi NP-kovia ongelmia ei voi ratkaista polynomisessa ajassa, mikĂ€li yleinen vaativuusolettamus P ≠ NP pitÀÀ paikkansa. TĂ€stĂ€ huolimatta haluaisimme kuitenkin usein ratkaista tĂ€llaisia vaikeita ongelmia. Kaksi yleistĂ€ lĂ€hestymistapaa vaikeiden, polynomisessa ajassa ratkeamattomien ongelmien tarkkaan ratkaisemiseen on (i) eksponentiaalinen algoritmiikka ja (ii) parametrisoitu algoritmiikka. Eksponentiaaliaikaisessa algoritmiikassa kehitetÀÀn algoritmeja, joiden ajoaika on edelleen eksponentiaalinen syötteen koon suhteen, mutta jotka vĂ€lttĂ€vĂ€t koko ratkaisuavaruuden lĂ€pikĂ€ynnin; toisin sanoen, kyse on vĂ€hemmĂ€n eksponentiaalisten algoritmien kehittĂ€misestĂ€. Parametrisoitu algoritmiikka puolestaan pyrkii eristĂ€mÀÀn eksponentiaaliaikaisen riippuvuuden ajoajassa syötteen koosta riippumattomaan parametriin. TĂ€ssĂ€ vĂ€itöstyössĂ€ esitetÀÀn eksponentiaaliaikaisia ja parametrisoituja algoritmeja erinĂ€isten vaikeiden verkko- ja hyperverkko-ongelmien tarkkaan ratkaisemiseen. Esitetyt algoritmit perustuvat kahteen algoritmiseen tekniikkaan: (i) monilineaarimuotojen evaluoiminen yli erilaisten puolirengaiden ja (ii) kombinatoristen hajotelmien kĂ€yttö. Algoritmien lisĂ€ksi työssĂ€ tarkastellaan nĂ€ihin tekniikoihin liittyviĂ€ vaativuusteoreettisia kysymyksiĂ€, mikĂ€ auttaa ymmĂ€rtĂ€mÀÀn tekniikoiden rajoituksia ja toistaiseksi hyödyntĂ€mĂ€ttömiĂ€ mahdollisuuksia

    Assigning Channels Via the Meet-in-the-Middle Approach

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    Homomorphisms are a good basis for counting small subgraphs

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    We introduce graph motif parameters, a class of graph parameters that depend only on the frequencies of constant-size induced subgraphs. Classical works by Lov\'asz show that many interesting quantities have this form, including, for fixed graphs HH, the number of HH-copies (induced or not) in an input graph GG, and the number of homomorphisms from HH to GG. Using the framework of graph motif parameters, we obtain faster algorithms for counting subgraph copies of fixed graphs HH in host graphs GG: For graphs HH on kk edges, we show how to count subgraph copies of HH in time kO(k)⋅n0.174k+o(k)k^{O(k)}\cdot n^{0.174k + o(k)} by a surprisingly simple algorithm. This improves upon previously known running times, such as O(n0.91k+c)O(n^{0.91k + c}) time for kk-edge matchings or O(n0.46k+c)O(n^{0.46k + c}) time for kk-cycles. Furthermore, we prove a general complexity dichotomy for evaluating graph motif parameters: Given a class C\mathcal C of such parameters, we consider the problem of evaluating f∈Cf\in \mathcal C on input graphs GG, parameterized by the number of induced subgraphs that ff depends upon. For every recursively enumerable class C\mathcal C, we prove the above problem to be either FPT or #W[1]-hard, with an explicit dichotomy criterion. This allows us to recover known dichotomies for counting subgraphs, induced subgraphs, and homomorphisms in a uniform and simplified way, together with improved lower bounds. Finally, we extend graph motif parameters to colored subgraphs and prove a complexity trichotomy: For vertex-colored graphs HH and GG, where HH is from a fixed class H\mathcal H, we want to count color-preserving HH-copies in GG. We show that this problem is either polynomial-time solvable or FPT or #W[1]-hard, and that the FPT cases indeed need FPT time under reasonable assumptions.Comment: An extended abstract of this paper appears at STOC 201

    Discrete Geometry

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    The workshop on Discrete Geometry was attended by 53 participants, many of them young researchers. In 13 survey talks an overview of recent developments in Discrete Geometry was given. These talks were supplemented by 16 shorter talks in the afternoon, an open problem session and two special sessions. Mathematics Subject Classification (2000): 52Cxx. Abstract regular polytopes: recent developments. (Peter McMullen) Counting crossing-free configurations in the plane. (Micha Sharir) Geometry in additive combinatorics. (József Solymosi) Rigid components: geometric problems, combinatorial solutions. (Ileana Streinu) ‱ Forbidden patterns. (János Pach) ‱ Projected polytopes, Gale diagrams, and polyhedral surfaces. (Günter M. Ziegler) ‱ What is known about unit cubes? (Chuanming Zong) There were 16 shorter talks in the afternoon, an open problem session chaired by Jesús De Loera, and two special sessions: on geometric transversal theory (organized by Eli Goodman) and on a new release of the geometric software Cinderella (Jürgen Richter-Gebert). On the one hand, the contributions witnessed the progress the field provided in recent years, on the other hand, they also showed how many basic (and seemingly simple) questions are still far from being resolved. The program left enough time to use the stimulating atmosphere of the Oberwolfach facilities for fruitful interaction between the participants
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