Combinatorial Insights Research Articles

On the Minimum Bisection of Random 3-Regular Graphs

In this paper we give new bounds on the bisection width of random 3-regular graphs on $n$ vertices. The main contribution is a new lower bound of $0.103295n$ based on a first moment method together with a structural analysis of the graph, thereby improving a 27-year-old result of Kostochka and Melnikov. We also give a complementary upper bound of $0.139822n$ by combining a result of Lyons with original combinatorial insights. Developping this approach further, we obtain a non-rigorous improved upper bound with the help of Monte Carlo simulations.

Combinatorial Properties and Recognition of Unit Square Visibility Graphs

Unit square visibility graphs (USV) are described by axis-parallel visibility between unit squares placed in the plane. If the squares are required to be placed on integer grid coordinates, then USV become unit square grid visibility graphs (USGV), an alternative characterisation of the well-known rectilinear graphs. We extend known combinatorial results for USGV and we show that, in the weak case (i.e., visibilities do not necessarily translate into edges of the represented combinatorial graph), the area minimisation variant of their recognition problem is {{,mathrm{{textsf{N}}{textsf{P}}},}}-hard. We also provide combinatorial insights with respect to USV, and as our main result, we prove their recognition problem to be {{,mathrm{{textsf{N}}{textsf{P}}},}}-hard, which settles an open question.

Tolerant Testers of Image Properties

We initiate a systematic study of tolerant testers of image properties or, equivalently, algorithms that approximate the distance from a given image to the desired property. Image processing is a particularly compelling area of applications for sublinear-time algorithms and, specifically, property testing. However, for testing algorithms to reach their full potential in image processing, they have to be tolerant, which allows them to be resilient to noise. We design efficient approximation algorithms for the following fundamental questions: What fraction of pixels have to be changed in an image so it becomes a half-plane? A representation of a convex object? A representation of a connected object? More precisely, our algorithms approximate the distance to three basic properties (being a half-plane, convexity, and connectedness) within a small additive error ε, after reading poly (1/ε) pixels, independent of the image size. We also design an efficient agnostic proper PAC learner of convex sets (continuous and discrete) in two dimensions under the uniform distribution. Our algorithms require very simple access to the input: uniform random samples for the half-plane property and convexity, and samples from uniformly random blocks for connectedness. However, the analysis of the algorithms, especially for convexity, requires many geometric and combinatorial insights. For example, in the analysis of the algorithm for convexity, we define a set of reference polygons P ε such that (1) every convex image has a nearby polygon in P ε and (2) one can use dynamic programming to quickly compute the smallest empirical distance to a polygon in P ε .

Role and therapeutic implications of protein glycosylation in neuroinflammation.

The importance of glycosylation (post-translational attachment of glycan residues to proteins) in the context of neuroinflammation is only now beginning to be understood. Although the glycome is challenging to investigate due to its complexity, this field is gaining interest because of the emergence of novel analytical methods. These investigations offer the possibility of further understanding the molecular signature of disorders with underlying neuroinflammatory cascades. In this review, we portray the clinically relevant trends in glyconeurobiology and suggest glyco-related paths that could be targeted therapeutically to decrease neuroinflammation. A combinatorial insight from glycobiology and neurology can be harnessed to better understand neuroinflammatory-related conditions to identify relevant molecular targets.

Reflections on kernelizing and computing unrooted agreement forests

Phylogenetic trees are leaf-labelled trees used to model the evolution of species. Here we explore the practical impact of kernelization (i.e. data reduction) on the NP-hard problem of computing the TBR distance between two unrooted binary phylogenetic trees. This problem is better-known in the literature as the maximum agreement forest problem, where the goal is to partition the two trees into a minimum number of common, non-overlapping subtrees. We have implemented two well-known reduction rules, the subtree and chain reduction, and five more recent, theoretically stronger reduction rules, and compare the reduction achieved with and without the stronger rules. We find that the new rules yield smaller reduced instances and thus have clear practical added value. In many cases they also cause the TBR distance to decrease in a controlled fashion, which can further facilitate solving the problem in practice. Next, we compare the achieved reduction to the known worst-case theoretical bounds of 15k-9 and 11k-9 respectively, on the number of leaves of the two reduced trees, where k is the TBR distance, observing in both cases a far larger reduction in practice. As a by-product of our experimental framework we obtain a number of new insights into the actual computation of TBR distance. We find, for example, that very strong lower bounds on TBR distance can be obtained efficiently by randomly sampling certain carefully constructed partitions of the leaf labels, and identify instances which seem particularly challenging to solve exactly. The reduction rules have been implemented within our new solver Tubro which combines kernelization with an Integer Linear Programming (ILP) approach. Tubro also incorporates a number of additional features, such as a cluster reduction and a practical upper-bounding heuristic, and it can leverage combinatorial insights emerging from the proofs of correctness of the reduction rules to simplify the ILP.

String periods in the order-preserving model

In the order-preserving model, two strings match if they share the same relative order between the characters at the corresponding positions. This model is quite recent, but it has already attracted significant attention because of its applications in data analysis. We introduce several types of periods in this setting (op-periods). Then we give algorithms to compute these periods in time O(n), O(nlog⁡log⁡n), O(nlog2⁡log⁡n/log⁡log⁡log⁡n), O(nlog⁡n) depending on the type of periodicity. In the most general variant, the number of different op-periods can be as big as Ω(n2), and a compact representation is needed. Our algorithms require novel combinatorial insight into the properties of op-periods. In particular, we characterize the Fine–Wilf property for coprime op-periods.

Minmax Regret k-Sink Location on a Dynamic Path Network with Uniform Capacities

In Dynamic flow networks an edge’s capacity is the amount of flow (items) that can enter it in unit time. All flow must be moved to sinks and congestion occurs if flow has to wait at a vertex for other flow to leave. In the uniform capacity variant all edges have the same capacity. The minmax k-sink location problem is to place k sinks minimizing the maximum time before all items initially on vertices can be evacuated to a sink. The minmax regret version introduces uncertainty into the input; the amount at each source is now only specified to within a range. The problem is to find a universal solution (placement of k sinks) whose regret (difference from optimal for a given input) is minimized over all inputs consistent with the given range restrictions. The previous best algorithms for the minmax regret version of the k-sink location problem on a path with uniform capacities ran in O(n) time for $$k=1$$ , $$O(n \log ^ 4 n)$$ time for $$k=2$$ and $${\varOmega }(n^{k+1})$$ for $$ k >2$$ . A major bottleneck to improving those solutions was that minmax regret seemed an inherently global property. This paper derives new combinatorial insights that allow decomposition into local problems. This permits designing two new algorithms. The first runs in $$O(n^3 \log n)$$ time independent of k and the second in $$O( n k^2 \log ^{k+1} n)$$ time. These improve all previous algorithms for $$k >1$$ and, for $$k > 2$$ , are the first polynomial time algorithms known.

Tree formulas, mean first passage times and Kemeny’s constant of a Markov chain

In this paper, we aim to provide probabilistic and combinatorial insights into tree formulas for the Green function and hitting probabilities of Markov chains on a finite state space. These tree formulas are closely related to loop-erased random walks by Wilson's algorithm for random spanning trees, and to mixing times by the Markov chain tree theorem. Let $m_{ij}$ be the mean first passage time from $i$ to $j$ for an irreducible chain with finite state space $S$ and transition matrix $(p_{ij}; i, j \in S)$. It is well-known that $m_{jj} = 1/\pi_j = \Sigma^{(1)}/\Sigma_j$, where $\pi$ is the stationary distribution for the chain, $\Sigma_j$ is the tree sum, over $n^{n-2}$ trees $\textbf{t}$ spanning $S$ with root $j$ and edges $i \rightarrow k$ directed to $j$, of the tree product $\prod_{i \rightarrow k \in \textbf{t} }p_{ik}$, and $\Sigma^{(1)}:= \sum_{j \in S} \Sigma_j$. Chebotarev and Agaev derived further results from {\em Kirchhoff's matrix tree theorem}. We deduce that for $i \ne j$, $m_{ij} = \Sigma_{ij}/\Sigma_j$, where $\Sigma_{ij}$ is the sum over the same set of $n^{n-2}$ spanning trees of the same tree product as for $\Sigma_j$, except that in each product the factor $p_{kj}$ is omitted where $k = k(i,j,\textbf{t})$ is the last state before $j$ in the path from $i$ to $j$ in $\textbf{t}$. It follows that Kemeny's constant $\sum_{j \in S} m_{ij}/m_{jj}$ equals to $ \Sigma^{(2)}/\Sigma^{(1)}$, where $\Sigma^{(r)}$ is the sum, over all forests $\textbf{f}$ labeled by $S$ with $r$ trees, of the product of $p_{ij}$ over edges $i \rightarrow j$ of $\textbf{t}$. We show that these results can be derived without appeal to the matrix tree theorem. A list of relevant literature is also reviewed.

Prize‐collecting set multicovering with submodular pricing

AbstractWe consider a ground set E and a submodular function acting on it. We first propose a “set multicovering” problem in which the value (price) of any is . We show that the linear program (LP) of this problem can be directly solved by applying a submodular function minimization (SFM) algorithm on the dual LP. However, the main results of this study concern prize‐collecting multicovering with submodular pricing, that is, a more general and more difficult “multicovering” problem in which elements can be left uncovered by paying a penalty. We formulate it as a large‐scale LP (with variables representing all subsets of E) that could be tackled by column generation (CG; for a CG algorithm for “set‐covering” problems with submodular pricing). However, we do not solve this large‐scale LP by CG, but we solve it in polynomial time by exploiting its integrality properties. More exactly, after appropriate restructuring, the dual LP can be transformed into an LP that has been thoroughly studied (as a primal) in the SFM theory. Solving this LP reduces to optimizing a strong map of submodular functions. For this, we use the Fleischer–Iwata framework that optimizes all these functions within the same asymptotic running time as solving a single SFM, that is, in , where and γ is the complexity of one submodular evaluation. Besides solving the problem, the proposed approach can be useful to (a) simultaneously find the best solution of up to functions under strong map relations in time, (b) perform sensitivity analysis in very short time (even at no extra cost), and (c) reveal combinatorial insight into the primal–dual optimal solutions. We present several potential applications throughout the paper, from production planning to combinatorial auctions.

Data reductions and combinatorial bounds for improved approximation algorithms

Kernelization algorithms in the context of Parameterized Complexity are often based on a combination of data reduction rules and combinatorial insights. We will expose in this paper a similar strategy for obtaining polynomial-time approximation algorithms. Our method features the use of approximation-preserving reductions, akin to the notion of parameterized reductions. We exemplify this method to obtain the currently best approximation algorithms for Harmless Set, Differential and Multiple Nonblocker, all of them can be considered in the context of securing networks or information propagation.

Combinatorial insights into doping control and transport properties of zinc tin nitride

A combinatorial study on semiconducting ZnSnN2, in which the lowest carrier density yet reported for as-deposited films is achieved, wurtzite crystal structure is confirmed, and optical bandgap is established at 1.0 eV.

Validating the Knuth-Morris-Pratt Failure Function, Fast and Online

Let $\pi'_{w}$ denote the failure function of the Knuth-Morris-Pratt algorithm for a word w. In this paper we study the following problem: given an integer array $A'[1 \mathinner {\ldotp \ldotp }n]$ , is there a word w over an arbitrary alphabet Σ such that $A'[i]=\pi'_{w}[i]$ for all i? Moreover, what is the minimum cardinality of Σ required? We give an elementary and self-contained $\mathcal{O}(n\log n)$ time algorithm for this problem, thus improving the previously known solution (Duval et al. in Conference in honor of Donald E. Knuth, 2007), which had no polynomial time bound. Using both deeper combinatorial insight into the structure of π′ and advanced algorithmic tools, we further improve the running time to $\mathcal{O}(n)$ .

Hierarchical Clustering of Dynamical Networks Using a Saddle-Point Analysis

This paper studies cluster synchronization in dynamical networks. A class of cooperative dynamical networks that exhibit clustering in their asymptotic behavior is analyzed. The network nodes are equipped with heterogeneous dynamics and interact with a nonlinear and saturated interaction rule. It is proven that cluster synchronization appears asymptotically independent of the initial conditions. The clustering behavior of the dynamic network is shown to correspond to the solution of a static saddle-point problem, enabling a precise characterization of the clustering structure. We show how the clustering structure depends on the relation between the underlying graph, the node dynamics, and the saturation level of the interactions. This interpretation leads to deeper combinatorial insights related to clustering, including a generalization of classical network partitioning problems such as the inhibiting bisection problem, the min <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</i> - <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</i> -cut problem, and hierarchical clustering analysis. The theoretical results are applied for the analysis of a test-case network, inspired by the IEEE 30-bus system.

Escaping Development of Drug-Resistant Mutants: Basis for Effective Chemotherapy of Enterovirus Infections

Based on a unified approach via the classical Burnside Lemma in combinatorics, we deal with some problems arising from symbolic dynamics and biology. The MSS sequences play an important role in symbolic dynamics, and they are in one-to-one correspondence with necklaces of beads with two colors. This necklace problem was studied by Fine, Gilbert, and Riordan in the 1950s and 1960s. Recently, a problem on the classification of the DNA sequence arose in biology and was studied by Bell, Torney, and others. In this paper, we present a further treatment of the necklace problem which shows a closer connection with MSS sequences and brings more combinatorial insight into the properties of symbolic dynamics. We also give a clear derivation of the formula for the classification of DNA sequences. The techniques used in this paper originate principally from properties of cycle structures, which are related to the fixed points of then-cube recently studied by Chen and Stanley.

Discontinuities in pattern inference

This paper deals with the inferrability of classes of E-pattern languages–also referred to as extended or erasing pattern languages–from positive data in Gold’s model of identification in the limit. The first main part of the paper shows that the recently presented negative result on terminal-free E-pattern languages over binary alphabets does not hold for other alphabet sizes, so that the full class of these languages is inferrable from positive data if and only if the corresponding terminal alphabet does not consist of exactly two distinct letters. The second main part yields the insight that the positive result on terminal-free E-pattern languages over alphabets with three or four letters cannot be extended to the class of general E-pattern languages. With regard to larger alphabets, the extensibility remains open. The proof methods developed for these main results do not directly discuss the (non-)existence of appropriate learning strategies, but they deal with structural properties of classes of E-pattern languages, and, in particular, with the problem of finding telltales for these languages. It is shown that the inferrability of classes of E-pattern languages is closely connected to some problems on the ambiguity of morphisms so that the technical contributions of the paper largely consist of combinatorial insights into morphisms in word monoids.

Paint It Black—A Combinatorial Yawp

Have you experienced a “mathematical yawp” lately? (Not sure you want to answer until you know what one is?) Well, the phrase “mathematical yawp” was coined by Francis Su in his James R. Leitzel Lecture at the 2006 MathFest. In essence, a mathematical yawp is one of those “light bulb” or “aha!” moments when a mathematician comes to an understanding of a topic so moving that it is accompanied by a yelp of joy or disbelief. By specialization, a combinatorial yawp is one of those moments achieved while counting. Combinatorial proofs are appreciated for the elegance and/or simplicity of their arguments (see [2]). However, the true (and frequently underappreciated) beauty lies in their power to generalize results. Understanding the components of a mathematical identity in a concrete counting context provides the first clue for exploring natural extensions. Investigating and stretching the role of each parameter in turn, leads to different generalizations—ones that might not be connected without the combinatorial insight. Our yawp occurred while exploring Problem # 11220, proposed by David Beckwith, from the April 2006 issue of the American Mathematical Monthly [1], the innocuous-looking alternating binomial identity below.

An efficiently computed lower bound on the number of recombinations in phylogenetic networks: Theory and empirical study

Phylogenetic networks are models of sequence evolution that go beyond trees, allowing biological operations that are not tree-like. One of the most important biological operations is recombination between two sequences. An established problem [J. Hein, Reconstructing evolution of sequences subject to recombination using parsimony, Math. Biosci. 98 (1990) 185–200; J. Hein, A heuristic method to reconstruct the history of sequences subject to recombination, J. Molecular Evoluation 36 (1993) 396–405; Y. Song, J. Hein, Parsimonious reconstruction of sequence evolution and haplotype blocks: finding the minimum number of recombination events, in: Proceedings of 2003 Workshop on Algorithms in Bioinformatics, Berlin, Germany, 2003, Lecture Notes in Computer Science, Springer, Berlin; Y. Song, J. Hein, On the minimum number of recombination events in the evolutionary history of DNA sequences, J. Math. Biol. 48 (2003) 160–186; L. Wang, K. Zhang, L. Zhang, Perfect phylogenetic networks with recombination, J. Comput. Biol. 8 (2001) 69–78; S.R. Myers, R.C. Griffiths, Bounds on the minimum number of recombination events in a sample history, Genetics 163 (2003) 375–394; V. Bafna, V. Bansal, Improved recombination lower bounds for haplotype data, in: Proceedings of RECOMB, 2005; Y. Song, Y. Wu, D. Gusfield, Efficient computation of close lower and upper bounds on the minimum number of needed recombinations in the evolution of biological sequences, Bioinformatics 21 (2005) i413–i422. Bioinformatics (Suppl. 1), Proceedings of ISMB, 2005, D. Gusfield, S. Eddhu, C. Langley, Optimal, efficient reconstruction of phylogenetic networks with constrained recombination, J. Bioinform. Comput. Biol. 2(1) (2004) 173–213; D. Gusfield, Optimal, efficient reconstruction of root-unknown phylogenetic networks with constrained and structured recombination, J. Comput. Systems Sci. 70 (2005) 381–398] is to find a phylogenetic network that derives an input set of sequences, minimizing the number of recombinations used. No efficient, general algorithm is known for this problem. Several papers consider the problem of computing a lower bound on the number of recombinations needed. In this paper we establish a new, efficiently computed lower bound. This result is useful in methods to estimate the number of needed recombinations, and also to prove the optimality of algorithms for constructing phylogenetic networks under certain conditions [D. Gusfield, S. Eddhu, C. Langley, Optimal, efficient reconstruction of phylogenetic networks with constrained recombination, J. Bioinform. Comput. Biol. 2(1) (2004) 173–213; D. Gusfield, Optimal, efficient reconstruction of root-unknown phylogenetic networks with constrained and structured recombination, J. Comput. Systems Sci. 70 (2005) 381–398; D. Gusfield, Optimal, efficient reconstruction of root-unknown phylogenetic networks with constrained recombination, Technical Report, Department of Computer Science, University of California, Davis, CA, 2004]. The lower bound is based on a structural, combinatorial insight, using only the site conflicts and incompatibilities, and hence it is fundamental and applicable to many biological phenomena other than recombination, for example, when gene conversions or recurrent or back mutations or cross-species hybridizations cause the phylogenetic history to deviate from a tree structure. In addition to establishing the bound, we examine its use in more complex lower bound methods, and compare the bounds obtained to those obtained by other established lower bound methods.

Local ratio

The local ratio technique is a methodology for the design and analysis of algorithms for a broad range of optimization problems. The technique is remarkably simple and elegant, and yet can be applied to several classical and fundamental problems (including covering problems, packing problems, and scheduling problems). The local ratio technique uses elementary math and requires combinatorial insight into the structure and properties of the problem at hand. Typically, when using the technique, one has to invent a weight function for a problem instance under which every "reasonable" solution is "good." The local ratio technique is closely related to the primal-dual schema, though it is not based on weak LP duality (which is the basis of the primal-dual approach) since it is not based on linear programming.In this survey we, introduce the local ratio technique and demonstrate its use in the design and analysis of algorithms for various problems. We trace the evolution path of the technique since its inception in the 1980's, culminating with the most recent development, namely, fractional local ratio , which can be viewed as a new LP rounding technique.

Necklaces, MSS Sequences, and DNA Sequences

Based on a unified approach via the classical Burnside Lemma in combinatorics, we deal with some problems arising from symbolic dynamics and biology. The MSS sequences play an important role in symbolic dynamics, and they are in one-to-one correspondence with necklaces of beads with two colors. This necklace problem was studied by Fine, Gilbert, and Riordan in the 1950s and 1960s. Recently, a problem on the classification of the DNA sequence arose in biology and was studied by Bell, Torney, and others. In this paper, we present a further treatment of the necklace problem which shows a closer connection with MSS sequences and brings more combinatorial insight into the properties of symbolic dynamics. We also give a clear derivation of the formula for the classification of DNA sequences. The techniques used in this paper originate principally from properties of cycle structures, which are related to the fixed points of then-cube recently studied by Chen and Stanley.

The complexity of type inference for higher-order typed lambda calculi

AbstractWe analyse the computational complexity of type inference for untyped λ-terms in the second-order polymorphic typed λ-calculus (F2) invented by Girard and Reynolds, as well as higher-order extensionsF3,F4, …,Fωproposed by Girard. We prove that recognising theF2-typable terms requires exponential time, and forFωthe problem is non-elementary. We show as well a sequence of lower bounds on recognising theFk-typable terms, where the bound forFk+1is exponentially larger than that forFk.The lower bounds are based on generic simulation of Turing Machines, where computation is simulated at the expression and type level simultaneously. Non-accepting computations are mapped to non-normalising reduction sequences, and hence non-typable terms. The accepting computations are mapped to typable terms, where higher-order types encode reduction sequences, and first-order types encode the entire computation as a circuit, based on a unification simulation of Boolean logic. A primary technical tool in this reduction is the composition of polymorphic functions having different domains and ranges.These results are the first nontrivial lower bounds on type inference for the Girard/Reynolds system as well as its higher-order extensions. We hope that the analysis provides important combinatorial insights which will prove useful in the ultimate resolution of the complexity of the type inference problem.