Solving graph connectivity problems on JAGs

Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1994.Includes bibliographical references (leaf 53).by Parry Husbands.M.S

Incremental branching programs

We propose a new model of restricted branching programs which we call {em incremental branching programs}. We show that {em syntactic} incremental branching programs capture previously studied structured models of computation for the problem GEN, namely marking machines [Cook74]. and Poon\u27s extension [Poon93] of jumping automata on graphs [CookRackoff80]. We then prove exponential size lower bounds for our syntactic incremental model, and for some other restricted branching program models as well. We further show that nondeterministic syntactic incremental branching programs are provably stronger than their deterministic counterpart when solving a natural NL-complete GEN subproblem. It remains open if syntactic incremental branching programs are as powerful as unrestricted branching programs for GEN problems. Joint work with Anna Gál and Michal KouckÃ

Space Complexity of the Directed Reachability Problem over Surface-Embedded Graphs

The graph reachability problem, the computational task of deciding whether there is a path between two given nodes in a graph is the canonical problem for studying space bounded computations. Three central open questions regarding the space complexity of the reachabil-ity problem over directed graphs are: (1) improving Savitch’s O(log2 n) space bound, (2) designing a polynomial-time algorithm with O(n1−) space bound, and (3) designing an unambiguous non-deterministic log-space (UL) algorithm. These are well-known open questions in complex-ity theory, and solving any one of them will be a major breakthrough. We will discuss some of the recent progress reported on these questions for certain subclasses of surface-embedded directed graphs

Sublinear-Space Lexicographic Depth-First Search for Bounded Treewidth Graphs and Planar Graphs

The lexicographic depth-first search (Lex-DFS) is one of the first basic graph problems studied in the context of space-efficient algorithms. It is shown independently by Asano et al. [ISAAC 2014] and Elmasry et al. [STACS 2015] that Lex-DFS admits polynomial-time algorithms that run with O(n)-bit working memory, where n is the number of vertices in the graph. Lex-DFS is known to be P-complete under logspace reduction, and giving or ruling out polynomial-time sublinear-space algorithms for Lex-DFS on general graphs is quite challenging. In this paper, we study Lex-DFS on graphs of bounded treewidth. We first show that given a tree decomposition of width O(n^(1-?)) with ? > 0, Lex-DFS can be solved in sublinear space. We then complement this result by presenting a space-efficient algorithm that can compute, for w ? ?n, a tree decomposition of width O(w ?nlog n) or correctly decide that the graph has a treewidth more than w. This algorithm itself would be of independent interest as the first space-efficient algorithm for computing a tree decomposition of moderate (small but non-constant) width. By combining these results, we can show in particular that graphs of treewidth O(n^(1/2 - ?)) for some ? > 0 admits a polynomial-time sublinear-space algorithm for Lex-DFS. We can also show that planar graphs admit a polynomial-time algorithm with O(n^(1/2+?))-bit working memory for Lex-DFS

New Time-Space Upperbounds for Directed Reachability in High-genus and H-minor-free Graphs

We obtain the following new simultaneous time-space upper bounds for the directed reachability problem. (1) A polynomial-time, O(n^{2/3} * g^{1/3})-space algorithm for directed graphs embedded on orientable surfaces of genus g. (2) A polynomial-time, O(n^{2/3})-space algorithm for all H-minor-free graphs given the tree decomposition, and (3) for K_{3,3}-free and K_5-free graphs, a polynomial-time, O(n^{1/2 + epsilon})-space algorithm, for every epsilon > 0. For the general directed reachability problem, the best known simultaneous time-space upper bound is the BBRS bound, due to Barnes, Buss, Ruzzo, and Schieber, which achieves a space bound of O(n/2^{k * sqrt(log(n))}) with polynomial running time, for any constant k. It is a significant open question to improve this bound for reachability over general directed graphs. Our algorithms beat the BBRS bound for graphs embedded on surfaces of genus n/2^{omega(sqrt(log(n))}, and for all H-minor-free graphs. This significantly broadens the class of directed graphs for which the BBRS bound can be improved

An O(n 1 2 +ɛ)-Space and Polynomial-Time Algorithm for Directed Planar Reachability

Abstract—We show that the reachability problem over directed planar graphs can be solved simultaneously in polynomial time and approximately O ( √ n) space. In contrast, the best space bound known for the reachability problem on general directed graphs with polynomial running time is O(n/2 √ log n Keywords-reachability, directed planar graph, sublinear space, polynomial time I

On space efficiency of algorithms working on structural decompositions of graphs

Dynamic programming on path and tree decompositions of graphs is a technique that is ubiquitous in the field of parameterized and exponential-time algorithms. However, one of its drawbacks is that the space usage is exponential in the decomposition's width. Following the work of Allender et al. [Theory of Computing, '14], we investigate whether this space complexity explosion is unavoidable. Using the idea of reparameterization of Cai and Juedes [J. Comput. Syst. Sci., '03], we prove that the question is closely related to a conjecture that the Longest Common Subsequence problem parameterized by the number of input strings does not admit an algorithm that simultaneously uses XP time and FPT space. Moreover, we complete the complexity landscape sketched for pathwidth and treewidth by Allender et al. by considering the parameter tree-depth. We prove that computations on tree-depth decompositions correspond to a model of non-deterministic machines that work in polynomial time and logarithmic space, with access to an auxiliary stack of maximum height equal to the decomposition's depth. Together with the results of Allender et al., this describes a hierarchy of complexity classes for polynomial-time non-deterministic machines with different restrictions on the access to working space, which mirrors the classic relations between treewidth, pathwidth, and tree-depth.Comment: An extended abstract appeared in the proceedings of STACS'16. The new version is augmented with a space-efficient algorithm for Dominating Set using the Chinese remainder theore

Bounds on monotone switching networks for directed connectivity

We separate monotone analogues of L and NL by proving that any monotone switching network solving directed connectivity on $n$ vertices must have size at least $n^(\Omega(\lg(n)))$.Comment: 49 pages, 12 figure

Kreisplanarität von Level-Graphen

In this dissertation we generalise the notion of level planar graphs in two directions: track planarity and radial planarity. Our main results are linear time algorithms both for the planarity test and for the computation of an embedding, and thus a drawing. Our algorithms use and generalise PQ-trees, which are a data structure for efficient planarity tests.In dieser Arbeit wird der Begriff Level-Planarität von Graphen auf zwei Arten erweitert: Spur-Planarität und radiale Level-Planarität. Die Hauptergebnisse sind Linearzeitalgorithmen zum Testen dieser Arten von Planarität und zur Erstellung einer entsprechenden Einbettung und somit einer Zeichnung. Die Algorithmen verwenden und generalisieren PQ-Bäume, eine bei effizienten Planaritätstests verwendete Datenstruktur

Estimating user interaction probability for non-guaranteed display advertising

Billions of advertisements are displayed to internet users every hour, a market worth approximately $110 billion in 2013. The process of displaying advertisements to internet users is managed by advertising exchanges, automated systems which match advertisements to users while balancing conflicting advertiser, publisher, and user objectives. Real-time bidding is a recent development in the online advertising industry that allows more than one exchange (or demand-side platform) to bid for the right to deliver an ad to a specific user while that user is loading a webpage, creating a liquid market for ad impressions. Real-time bidding accounted for around 10% of the German online advertising market in late 2013, a figure which is growing at an annual rate of around 40%. In this competitive market, accurately calculating the expected value of displaying an ad to a user is essential for profitability. In this thesis, we develop a system that significantly improves the existing method for estimating the value of displaying an ad to a user in a German advertising exchange and demand-side platform. The most significant calculation in this system is estimating the probability of a user interacting with an ad in a given context. We first implement a hierarchical main-effects and latent factor model which is similar enough to the existing exchange system to allow a simple and robust upgrade path, while improving performance substantially. We then use regularized generalized linear models to estimate the probability of an ad interaction occurring following an individual user impression event. We build a system capable of training thousands of campaign models daily, handling over 300 million events per day, 18 million recurrent users, and thousands of model dimensions. Together, these systems improve on the log-likelihood of the existing method by over 10%. We also provide an overview of the real-time bidding market microstructure in the German real- time bidding market in September and November 2013, and indicate potential areas for exploiting competitors’ behaviour, including building user features from real-time bid responses. Finally, for personal interest, we experiment with scalable k-nearest neighbour search algorithms, nonlinear dimension reduction, manifold regularization, graph clustering, and stochastic block model inference using the large datasets from the linear model