Combinatorial Structure Research Articles

MV polytopes and reduced double Bruhat cells

When G is a complex reductive algebraic group, MV polytopes are in bijection with the non-negative tropical points of the unipotent group of G . By fixing w from the Weyl group, we can define MV polytopes whose highest vertex is labelled by w . We show that these polytopes are in bijection with the non-negative tropical points of the reduced double Bruhat cell labelled by w^{-1} . To do this, we define a collection of generalized minor functions \Delta_{\gamma}^{\mathrm{new}} which tropicalize on the reduced Bruhat cell to the BZ data of an MV polytope of highest vertex w . We also describe the combinatorial structure of MV polytopes of highest vertex w . We explicitly describe the map from the Weyl group to the subset of elements bounded by w in the Bruhat order which sends u \mapsto v if the vertex labelled by u coincides with the vertex labelled by v for every MV polytope of highest vertex w . As a consequence of this map, we prove that these polytopes have vertices labelled by Weyl group elements less than w in the Bruhat order.

Publisher's Note

Let A be an n×n real matrix. As shown in the recent paper S.M. Fallat, H.T. Hall, J.C.-H. Lin, and B.L. Shader (2022) [5], if the manifolds MA={G−1AG:G∈GL(n,R)} and Q(sgn(A)) (consisting of all real matrices having the same sign pattern as A), both considered as embedded submanifolds of Rn×n, intersect transversally at A, then every superpattern of sgn(A) also allows a matrix similar to A. Those authors introduced a condition on A (in terms of certain linear matrix equations) equivalent to the above transversality, called the nonsymmetric strong spectral property (nSSP). In this paper, this transversality property of A is characterized using an alternative, more direct and convenient condition, called the similarity-transversality property (STP). Let X=[xij] be a generic matrix of order n whose entries are independent variables. The STP of A is defined as the full row rank property of the Jacobian matrix of the entries of AX−XA at the zero entry positions of A with respect to the nondiagonal entries of X. This new approach makes it possible to take better advantage of the combinatorial structure of the matrix A, and provides theoretical foundation for constructing matrices similar to a given matrix while the entries have certain desired signs. In particular, several important classes of zero-nonzero patterns and sign patterns that require or allow this transversality property are identified. Examples illustrating many possible applications (such as diagonalizability, number of distinct eigenvalues, nilpotence, idempotence, semi-stability, eigenvalues and their algebraic and geometric multiplicities, Jordan canonical form, minimal polynomial, and rank) are provided.

An Attention-Based Method for the Minimum Vertex Cover Problem on Complex Networks

Solving combinatorial problems on complex networks represents a primary issue which, on a large scale, requires the use of heuristics and approximate algorithms. Recently, neural methods have been proposed in this context to find feasible solutions for relevant computational problems over graphs. However, such methods have some drawbacks: (1) they use the same neural architecture for different combinatorial problems without introducing customizations that reflects the specificity of each problem; (2) they only use a nodes local information to compute the solution; (3) they do not take advantage of common heuristics or exact algorithms. Following this interest, in this research we address these three main points by designing a customized attention-based mechanism that uses both local and global information from the adjacency matrix to find approximate solutions for the Minimum Vertex Cover Problem. We evaluate our proposal with respect to a fast two-factor approximation algorithm and a widely adopted state-of-the-art heuristic both on synthetically generated instances and on benchmark graphs with different scales. Experimental results demonstrate that, on the one hand, the proposed methodology is able to outperform both the two-factor approximation algorithm and the heuristic on the test datasets, scaling even better than the heuristic with harder instances and, on the other hand, is able to provide a representation of the nodes which reflects the combinatorial structure of the problem.

New constructions for disjoint partial difference families and external partial difference families

AbstractRecently, new combinatorial structures called disjoint partial difference families (DPDFs) and external partial difference families (EPDFs) were introduced, which simultaneously generalize partial difference sets, disjoint difference families and external difference families, and have applications in information security. So far, all known construction methods have used cyclotomy in finite fields. We present the first noncyclotomic infinite families of DPDFs which are also EPDFs, in structures other than finite fields (in particular cyclic groups and nonabelian groups). As well as direct constructions, we present an approach to constructing DPDFs/EPDFs using relative difference sets (RDSs); as part of this, we demonstrate how the well‐known RDS result of Bose extends to a very natural construction for DPDFs and EPDFs.

Existence of schematic arrays under a novel criterion

. As an important combinatorial structure, association scheme plays an important role in many fields, such as coding theory, graph theory, and combinatorial design. An array is called a schematic if its runs form an association scheme with respect to a certain classification criterion. However, at present, the existing research results about the schematic array are only based on the distance classification criterion, which dramatically limits the existing results on schematic arrays. This article presents a novel matching classification criterion, called MC-criterion, which, together with the existing distance classification, forms a general framework for classification. An array is called G-schematic if its runs form an association scheme with respect to MC-criterion. Subsequently, this article proves that the existence of two types of G-schematic arrays.

Searching for Structure: Characterizing the Protein Conformational Landscape with Clustering-Based Algorithms.

The identification and characterization of the main conformations from a protein population are a challenging and inherently high-dimensional problem. Here, we evaluate the performance of the Secondary sTructural Ensembles with machine LeArning (StELa) double-clustering method, which clusters protein structures based on the relationship between the φ and ψ dihedral angles in a protein backbone and the secondary structure of the protein, thus focusing on the local properties of protein structures. The classification of states as vectors composed of the clusters' indices arising naturally from the Ramachandran plot is followed by the hierarchical clustering of the vectors to allow for the identification of the main features of the corresponding free energy landscape (FEL). We compare the performance of StELa with the established root-mean-squared-deviation (RMSD)-based clustering algorithm, which focuses on global properties of protein structures and with Combinatorial Averaged Transient Structure (CATS), the combinatorial averaged transient structure clustering method based on distributions of the φ and ψ dihedral angle coordinates. Using ensembles of conformations from molecular dynamics simulations of intrinsically disordered proteins (IDPs) of various lengths (tau protein fragments) or short fragments from a globular protein, we show that StELa is the clustering method that identifies many of the minima and relevant energy states around the minima from the corresponding FELs. In contrast, the RMSD-based algorithm yields a large number of clusters that usually cover most of the FEL, thus being unable to distinguish between states, while CATS does not sample well the FELs for long IDPs and fragments from globular proteins.

On the generation of discrete figures with connectivity constraints

This paper addresses a generalization of polyominoes called (a, b)-connected discrete figures, where a and b represent the connectivity of the foreground (i.e. black pixels) and background (i.e. white pixels), respectively. Formally, a finite set of pixels P is (a, b)-connected if P is a-connected and P is b-connected. By adapting a combinatorial structure enumeration algorithm by J. L. Martin and employing breadth-first search ordering on the pixels of the figures, we sequentially generate all (a, b)-connected discrete figures up to size n = 18, utilizing minimal storage space. This paper presents an extended version of the research presented at the 2022 GASCom conference.

The giant leap to humankind

Penny (2009) urges that biologists stress more strongly the biological continuity between great apes and humans. Besides stressing genetic similarities, he suggests that great apes also share psychological capacities, including language and tool use. I argue that he underestimates the psychological differences. There is no evidence that great apes have anything approaching human language, or human technological capacity. A main ingredient of human cognition is recursion, which lifted communication to true combinatorial language and simple tool use to advanced combinatorial technology. Recursion may also explain the combinatorial structure of human memory, imagination, and theory of mind. Part of the key, as Penny recognises, may lie in the trajectory of human brain growth, but there is still much to understand in how micro-tweaking of the genome achieved such dramatic differences between ape and human.

Collective combinatorial optimisation as judgment aggregation

AbstractIn many settings, a collective decision has to be made over a set of alternatives that has a combinatorial structure: important examples are multi-winner elections, participatory budgeting, collective scheduling, and collective network design. A further common point of these settings is that agents generally submit preferences over issues (e.g., projects to be funded), each having a cost, and the goal is to find a feasible solution maximising the agents’ satisfaction under problem-specific constraints. We propose the use of judgment aggregation as a unifying framework to model these situations, which we refer to as collective combinatorial optimisation problems. Despite their shared underlying structure, collective combinatorial optimisation problems have so far been studied independently. Our formulation into judgment aggregation connects them, and we identify their shared structure via five case studies of well-known collective combinatorial optimisation problems, proving how popular rules independently defined for each problem actually coincide. We also chart the computational complexity gap that may arise when using a general judgment aggregation framework instead of a specific problem-dependent model.

Simulation of the Performance of the Financial Market of Ukraine in the Conditions of Overcoming Threats to Economic Security

The purpose of the article is the process of modeling the effectiveness of the financial market of Ukraine in the conditions of overcoming threats to economic security. Forecasting possible behavioral reactions to the integrated performance of the financial market of Ukraine is necessary for connection with the academic environment, the variability of risks formed by the content of influencing factors, by setting the volumes of critical operations to respond to the effect of influencing factors. This is because the volumes of critical financial market operations, according to its combinatorial structure, are also aimed at obtaining a result. Therefore, we suggest predicting possible behavioral reactions to the integrated performance of the financial market of Ukraine. The forecast is focused on obtaining reaction values regarding critical financial market operations for the next resolution/achievement of good target values. The received forecast of changes creates an opportunity with sufficient accuracy to reveal the conditions for the formation of results, notably that characteristic of critical operations of the financial market, using studies of its internal reactivity, which is guided by the action of influencing factors. According to the results of the performance evaluation, it is evident that the expected integral performance of the financial market of Ukraine will demonstrate a much lower expected increase in benefits due to the likely narrowing of the market capacity and reduction of investment options. The results of such analysis and forecasting will make it possible to reasonably approach the development of sets of priority actions within the framework of strategic maps, the fixation of outlined priorities, and predictive analysis of changes in their effectiveness over time (which provides visualization of the movement towards absorption or complete absorption of destabilizing processes in the financial market).

Abrupt grammatical reorganization of an emergent sign language

Abstract This study traces the development of discrete, combinatorial structure in Zinacantec Family Homesign (‘Z Sign’), a sign language developed since the 1970s by several deaf siblings in Mexico (Haviland 2020b), focusing on the expression of motion. The results reveal that the first signer, who generated a homesign system without access to language models, represents motion events holistically. Later-born signers, who acquired this homesign system from infancy, distribute the components of motion events over sequences of discrete signs. Furthermore, later-born signers exhibit greater regularity of form-meaning mappings and increased articulatory efficiency. Importantly, these changes occur abruptly between the first- and second-born signers, rather than incrementally across signers. This study extends previous findings for Nicaraguan Sign Language (Senghas et al. 2004) to a social group of a much smaller scale, suggesting that the parallel processes of cultural transmission and language acquisition drive language emergence, regardless of community size.

Navigating the realm of noncommutative probability: Historical perspectives and future directionsstorical perspectives and future directions

This review article provides a comprehensive overview of the fascinating field of noncommutative probability theory, tracing its evolution from its inception in the early 1980s by Romanian-American mathematician Dan Voiculescu to its current state of prominence in mathematics. Through a meticulous examination of seminal works and recent advancements, we explore the key concepts, methodologies, and significant developments in this field, emphasizing the combinatorial aspects of noncommutative probability spaces, including non-crossing partitions and linked partitions. This exploration encompasses various aspects, including analytical methods, operator algebras, random matrices, and combinatorial structures. Additionally, it concludes with the current understanding and potential directions for future research.

Combinatorial versus algebraic formulae for the Moore–Penrose inverse of a Laplacian matrix of a threshold graph

We present four different approaches to calculate the Moore–Penrose inverse of a Laplacian matrix of a connected threshold graph. The first one is combinatorial and it benefits from the combinatorial structure of a threshold graph, while the second three are of algebraic nature and explore the particular structure of a matrix in question.

Hessenberg-Toeplitz matrix determinants with Schröder and Fine number entries

In this paper, we find determinant formulas of several Hessenberg-Toeplitz matrices whose nonzero entries are derived from the small and large Schröder and Fine number sequences. Algebraic proofs of these results can be given which make use of Trudi's formula and the generating function of the associated sequence of determinants. We also provide direct arguments of our results that utilize various counting techniques, among them sign-changing involutions, on combinatorial structures related to classes of lattice paths enumerated by the Schröder and Fine numbers. As a consequence of our results, we obtain some new formulas for the Schröder and Catalan numbers as well as for some additional sequences from the OEIS in terms of determinants of certain Hessenberg-Toeplitz matrices.

Composition closed premodel structures and the Kreweras lattice

We investigate the rich combinatorial structure of premodel structures on finite lattices whose weak equivalences are closed under composition. We prove that there is a natural refinement of the inclusion order of weak factorization systems so that the intervals detect these composition closed premodel structures. In the case that the lattice in question is a finite total order, this natural order retrieves the Kreweras lattice of noncrossing partitions as a refinement of the Tamari lattice, and model structures can be identified with certain tricolored trees.

On the number of regions of piecewise linear neural networks

Many feedforward neural networks (NNs) generate continuous and piecewise-linear (CPWL) mappings. Specifically, they partition the input domain into regions on which the mapping is affine. The number of these so-called linear regions offers a natural metric to characterize the expressiveness of CPWL NNs. The precise determination of this quantity is often out of reach in practice, and bounds have been proposed for specific architectures, including for ReLU and Maxout NNs. In this work, we generalize these bounds to NNs with arbitrary and possibly multivariate CPWL activation functions. We first provide upper and lower bounds on the maximal number of linear regions of a CPWL NN given its depth, width, and the number of linear regions of its activation functions. Our results rely on the combinatorial structure of convex partitions and confirm the distinctive role of depth which, on its own, is able to exponentially increase the number of regions. We then introduce a complementary stochastic framework to estimate the average number of linear regions produced by a CPWL NN. Under reasonable assumptions, the expected density of linear regions along any 1D path is bounded by the product of depth, width, and a measure of activation complexity (up to a scaling factor). This yields an identical role to the three sources of expressiveness: no exponential growth with depth is observed anymore.

Three New Refined Arnold Families

The Springer numbers, introduced by Arnold, are generalizations of Euler numbers in the sense of Coxeter groups. They appear as the row sums of a double triangular array $(v_{n,k})$ of integers, $1\leq|k|\leq n$, defined recursively by a boustrophedon algorithm. We say a sequence of combinatorial objects $(X_{n,k})$ is an Arnold family if $X_{n,k}$ is counted by $v_{n,k}$. A polynomial refinement $V_{n,k}(t)$ of $v_{n,k}$, together with the combinatorial interpretations in several combinatorial structures was introduced by Eu and Fu recently. In this paper, we provide three new Arnold families of combinatorial objects, namely the cycle-up-down permutations, the valley signed permutations and Knuth's flip equivalences on permutations. We shall find corresponding statistics to realize the refined polynomial arrays.

Tropical Compactification via Ganter’s Algorithm

AbstractWe describe a canonical compactification of a polyhedral complex in Euclidean space. When the recession cones of the polyhedral complex form a fan, the compactified polyhedral complex is a subspace of a tropical toric variety. In this case, the procedure is analogous to the tropical compactifications of subvarieties of tori. We give an analysis of the combinatorial structure of the compactification and show that its Hasse diagram can be computed via Ganter’s algorithm. Our algorithm is implemented in and shipped with .

Union-closed sets and Horn Boolean functions

A family F of sets is union-closed if the union of any two sets from F belongs to F. The union-closed sets conjecture states that if F is a finite union-closed family of finite sets, then there is an element that belongs to at least half of the sets in F. The conjecture has several equivalent formulations in terms of other combinatorial structures such as lattices and graphs. In its whole generality the conjecture remains wide open, but it was verified for various important classes of lattices, such as lower semimodular lattices, and graphs, such as chordal bipartite graphs. In the present paper we develop a Boolean approach to the conjecture and verify it for several classes of Boolean functions, such as submodular functions and double Horn functions.

Distance measures for geometric graphs

A geometric graph is a combinatorial graph, endowed with a geometry that is inherited from its embedding in a Euclidean space. Formulation of a meaningful measure of (dis-)similarity in both the combinatorial and geometric structures of two such geometric graphs is a challenging problem in pattern recognition. We study two notions of distance measures for geometric graphs, called the geometric edit distance (GED) and geometric graph distance (GGD). While the former is based on the idea of editing one graph to transform it into the other graph, the latter is inspired by inexact matching of the graphs. For decades, both notions have been lending themselves well as measures of similarity between attributed graphs. If used without any modification, however, they fail to provide a meaningful distance measure for geometric graphs—even cease to be a metric. We have curated their associated cost functions for the context of geometric graphs. Alongside studying the metric properties of GED and GGD, we investigate how the two notions compare. We further our understanding of the computational aspects of GGD by showing that the distance is NP-hard to compute, even if the graphs are planar and arbitrary cost coefficients are allowed.As a computationally tractable alternative, we propose in this paper the Graph Mover's Distance (GMD), which has been formulated as an instance of the earth mover's distance. The computation of the GMD between two geometric graphs with at most n vertices takes only O(n3)-time. The GMD demonstrates extremely promising empirical evidence at recognizing letter drawings.