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Loss-Based Variational Bayes Prediction J. Comput. Graph. Stat. (IF 2.4) Pub Date : 2024-04-16 David T. Frazier, Rubén Loaiza-Maya, Gael M. Martin, Bonsoo Koo
We propose a new approach to Bayesian prediction that caters for models with a large number of parameters and is robust to model misspecification. Given a class of high-dimensional (but parametric)...
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Maximum size of a triangle-free graph with bounded maximum degree and matching number J. Comb. Optim. (IF 1.0) Pub Date : 2024-04-16 Milad Ahanjideh, Tınaz Ekim, Mehmet Akif Yıldız
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Wavelet feature screening J. Comput. Graph. Stat. (IF 2.4) Pub Date : 2024-04-15 Rodney Fonseca, Pedro Morettin, Aluísio Pinheiro
An initial screening of which covariates are relevant is a common practice in high-dimensional regression models. The classic feature screening selects only a subset of covariates correlated with t...
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Computational complexity and algorithms for two scheduling problems under linear constraints J. Comb. Optim. (IF 1.0) Pub Date : 2024-04-14 Kameng Nip, Peng Xie
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Branch-and-cut-and-price algorithm for the constrained-routing and spectrum assignment problem J. Comb. Optim. (IF 1.0) Pub Date : 2024-04-14 Ibrahima Diarrassouba, Youssouf Hadhbi, A. Ridha Mahjoub
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Data Nuggets: A Method for Reducing Big Data While Preserving Data Structure J. Comput. Graph. Stat. (IF 2.4) Pub Date : 2024-04-12 Traymon E. Beavers, Ge Cheng, Yajie Duan, Javier Cabrera, Mariusz Lubomirski, Dhammika Amaratunga, Jeffrey E. Teigler
Big data, with N×P dimension where N is extremely large, has created new challenges for data analysis, particularly in the realm of creating meaningful clusters of data. Clustering techniques, suc...
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Distributed Learning for Principal Eigenspaces without Moment Constraints J. Comput. Graph. Stat. (IF 2.4) Pub Date : 2024-04-12 Yong He, Zichen Liu, Yalin Wang
Distributed Principal Component Analysis (PCA) has been studied to deal with the case when data are stored across multiple machines and communication cost or privacy concerns prohibit the computati...
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A tight max-flow min-cut duality theorem for nonlinear multicommodity flows J. Comb. Optim. (IF 1.0) Pub Date : 2024-04-11 Matthew Broussard, Bala Krishnamoorthy
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Relative Entropy Gradient Sampler for Unnormalized Distribution J. Comput. Graph. Stat. (IF 2.4) Pub Date : 2024-04-09 Xingdong Feng, Yuan Gao, Jian Huang, Yuling Jiao, Xu Liu
We propose a relative entropy gradient sampler (REGS) for sampling from unnormalized distributions. REGS is a particle method that seeks a sequence of simple nonlinear transforms iteratively pushin...
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Minimizing the expense transmission time from the source node to demand nodes J. Comb. Optim. (IF 1.0) Pub Date : 2024-04-06 Mehdi Ghiyasvand, Iman Keshtkar
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n-fold L(2, 1)-labelings of Cartesian product of paths and cycles J. Comb. Optim. (IF 1.0) Pub Date : 2024-04-06 Fei-Huang Chang, Ma-Lian Chia, Shih-Ang Jiang, David Kuo, Jing-Ho Yan
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The average size of maximal matchings in graphs J. Comb. Optim. (IF 1.0) Pub Date : 2024-04-04 Alain Hertz, Sébastien Bonte, Gauvain Devillez, Hadrien Mélot
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An exact borderline between the NP-hard and polynomial-time solvable cases of flow shop scheduling with job-dependent storage requirements J. Comb. Optim. (IF 1.0) Pub Date : 2024-04-04 Alexander Kononov, Marina Pakulich
We consider two versions of two-machine flow shop scheduling problems, where each job requires an additional resource from the start of its first operation till the end of its second operation. We refer to this resource as storage space. The storage requirement of each job is determined by the processing time of its first operation. The two problems differ from each other in the way resources are allocated
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On inference for modularity statistics in structured networks J. Comput. Graph. Stat. (IF 2.4) Pub Date : 2024-04-01 Anirban Mitra, Konasale Prasad, Joshua Cape
This paper revisits the classical concept of network modularity and its spectral relaxations used throughout graph data analysis. We formulate and study several modularity statistic variants for wh...
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Randomized approximation schemes for minimizing the weighted makespan on identical parallel machines J. Comb. Optim. (IF 1.0) Pub Date : 2024-03-31 Ruiqing Sun
In this paper, we discuss scheduling problems with m identical machines and n jobs where each job has to be assigned to some machine. The objective is to minimize the weighted makespan of jobs, i.e., the maximum weighted completion time of jobs. This scheduling problem is a generalization of minimizing the makespan on parallel machine scheduling problem. We present a (\(2-\frac{1}{m}\))-approximation
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Selecting intervals to optimize the design of observational studies subject to fine balance constraints J. Comb. Optim. (IF 1.0) Pub Date : 2024-03-31
Abstract Motivated by designing observational studies using matching methods subject to fine balance constraints, we introduce a new optimization problem. This problem consists of two phases. In the first phase, the goal is to cluster the values of a continuous covariate into a limited number of intervals. In the second phase, we find the optimal matching subject to fine balance constraints with respect
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Online car-sharing problem with variable booking times J. Comb. Optim. (IF 1.0) Pub Date : 2024-03-30 Haodong Liu, Kelin Luo, Yinfeng Xu, Huili Zhang
In this paper, we address the problem of online car-sharing with variable booking times (CSV for short). In this scenario, customers submit ride requests, each specifying two important time parameters: the booking time and the pick-up time (start time), as well as two location parameters—the pick-up location and the drop-off location within a graph. For each request, it’s important to note that it
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Star covers and star partitions of double-split graphs J. Comb. Optim. (IF 1.0) Pub Date : 2024-03-22 Joyashree Mondal, S. Vijayakumar
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High-Dimensional Multivariate Linear Regression with Weighted Nuclear Norm Regularization J. Comput. Graph. Stat. (IF 2.4) Pub Date : 2024-03-13 Namjoon Suh, Li-Hsiang Lin, Xiaoming Huo
We consider a low-rank matrix estimation problem when the data is assumed to be generated from the multivariate linear regression model. To induce the low-rank coefficient matrix, we employ the wei...
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An exact game-theoretic variable importance index for generalized additive models J. Comput. Graph. Stat. (IF 2.4) Pub Date : 2024-03-13 Amir Khorrami Chokami, Giovanni Rabitti
Generalized Additive Models (GAMs) are widely used in statistics. In this work, we aim to tackle the challenge of identifying the most influential variables in GAMs. To accomplish this, we introduc...
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Variational Bayesian Neural Networks via Resolution of Singularities J. Comput. Graph. Stat. (IF 2.4) Pub Date : 2024-03-14 Susan Wei, Edmund Lau
In this work, we advocate for the importance of singular learning theory (SLT) as it pertains to the theory and practice of variational inference in Bayesian neural networks (BNNs). To begin, we la...
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Semiparametric Probit Regression Model with General Interval-Censored Failure Time Data J. Comput. Graph. Stat. (IF 2.4) Pub Date : 2024-03-12 Yi Deng, Shuwei Li, Liuquan Sun, Xinyuan Song
Interval-censored data frequently arise in various biomedical areas involving periodical follow-ups where the failure or event time of interest cannot be observed exactly but is only known to fall ...
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On list (p, 1)-total labellings of special planar graphs and 1-planar graphs J. Comb. Optim. (IF 1.0) Pub Date : 2024-03-13 Lin Sun, Guanglong Yu, Jianliang Wu
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Generative Quantile Regression with Variability Penalty J. Comput. Graph. Stat. (IF 2.4) Pub Date : 2024-03-08 Shijie Wang, Minsuk Shin, Ray Bai
Quantile regression and conditional density estimation can reveal structure that is missed by mean regression, such as multimodality and skewness. In this paper, we introduce a deep learning genera...
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Birational geometry of generalized Hessenberg varieties and the generalized Shareshian-Wachs conjecture J. Comb. Theory A (IF 1.1) Pub Date : 2024-03-04 Young-Hoon Kiem, Donggun Lee
We introduce generalized Hessenberg varieties and establish basic facts. We show that the Tymoczko action of the symmetric group on the cohomology of Hessenberg varieties extends to generalized Hessenberg varieties and that natural morphisms among them preserve the action. By analyzing natural morphisms and birational maps among generalized Hessenberg varieties, we give an elementary proof of the Shareshian-Wachs
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Independence-Encouraging Subsampling for Nonparametric Additive Models J. Comput. Graph. Stat. (IF 2.4) Pub Date : 2024-03-01 Yi Zhang, Lin Wang, Xiaoke Zhang, HaiYing Wang
The additive model is a popular nonparametric regression method due to its ability to retain modeling flexibility while avoiding the curse of dimensionality. The backfitting algorithm is an intuiti...
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A penalized criterion for selecting the number of clusters for K-medians J. Comput. Graph. Stat. (IF 2.4) Pub Date : 2024-02-29 Antoine Godichon-Baggioni, Sobihan Surendran
Clustering is a usual unsupervised machine learning technique for grouping the data points into groups based upon similar features. We focus here on unsupervised clustering for contaminated data, i...
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A multi-attribute evaluation of genotype-environment experiments using biplots and joint plots graphics J. Comput. Graph. Stat. (IF 2.4) Pub Date : 2024-02-29 Jhessica Leticia Kirch, Acácia Mecejana Diniz Souza Spitti, Alisson Fernando Chiorato, Carlos Tadeu dos Santos Dias, César Gonçalves de Lima
In plant breeding studies, some of objectives are to study the interaction between genotype and environment (GEI), evaluating genotypic stability and adaptability. The additive model with multiplic...
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Turán theorems for even cycles in random hypergraph J. Comb. Theory B (IF 1.4) Pub Date : 2024-03-01 Jiaxi Nie
Let be a family of -uniform hypergraphs. The random Turán number is the maximum number of edges in an -free subgraph of , where is the Erdős-Rényi random -graph with parameter . Let denote the -uniform linear cycle of length . For , Mubayi and Yepremyan showed that . This upper bound is not tight when . In this paper, we close the gap for . More precisely, we show that when . Similar results have recently
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The second largest eigenvalue of normal Cayley graphs on symmetric groups generated by cycles J. Comb. Theory A (IF 1.1) Pub Date : 2024-03-01 Yuxuan Li, Binzhou Xia, Sanming Zhou
We study the normal Cayley graphs on the symmetric group , where and is the set of all cycles in with length in . We prove that the strictly second largest eigenvalue of can only be achieved by at most four irreducible representations of , and we determine further the multiplicity of this eigenvalue in several special cases. As a corollary, in the case when contains neither nor we know exactly when
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A short combinatorial proof of dimension identities of Erickson and Hunziker J. Comb. Theory A (IF 1.1) Pub Date : 2024-02-29 Nishu Kumari
In a recent paper (), Erickson and Hunziker consider partitions in which the arm–leg difference is an arbitrary constant . In previous works, these partitions are called -asymmetric partitions. Regarding these partitions and their conjugates as highest weights, they prove an identity yielding an infinite family of dimension equalities between and modules. Their proof proceeds by the manipulations of
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A Deep Dynamic Latent Block Model for Co-clustering of Zero-Inflated Data Matrices J. Comput. Graph. Stat. (IF 2.4) Pub Date : 2024-02-23 Giulia Marchello, Marco Corneli, Charles Bouveyron
The simultaneous clustering of observations and features of data sets (known as co-clustering) has recently emerged as a central machine learning application to summarize massive data sets. However...
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The Journal of Computational and Graphical Statistics 2023 Associate Editors J. Comput. Graph. Stat. (IF 2.4) Pub Date : 2024-02-26
Published in Journal of Computational and Graphical Statistics (Vol. 33, No. 1, 2024)
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Approximation algorithms for the fault-tolerant facility location problem with submodular penalties J. Comb. Optim. (IF 1.0) Pub Date : 2024-02-26 Yingying Guo, Qiaoliang Li
This work is to discuss the fault-tolerant facility location problem with submodular penalties. We propose an LP-rounding 2.27-approximation algorithm, where every demand point j has a requirement that \(t_{j}\) distinct facilities serve it. This is the first constant performance guarantee known for this problem. In addition, we give an LP-rounding 2-approximation algorithm for the case where all requirements
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A linear ordering problem with weighted rank J. Comb. Optim. (IF 1.0) Pub Date : 2024-02-26 Manuel V. C. Vieira
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Forecasting high-dimensional functional time series: Application to sub-national age-specific mortality J. Comput. Graph. Stat. (IF 2.4) Pub Date : 2024-02-20 Cristian F. Jiménez-Varón, Ying Sun, Han Lin Shang
We study the modeling and forecasting of high-dimensional functional time series (HDFTS), which can be cross-sectionally correlated and temporally dependent. We introduce a decomposition of the HDF...
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On sufficient conditions for Hamiltonicity of graphs, and beyond J. Comb. Optim. (IF 1.0) Pub Date : 2024-02-23 Hechao Liu, Lihua You, Yufei Huang, Zenan Du
Identifying certain conditions that ensure the Hamiltonicity of graphs is highly important and valuable due to the fact that determining whether a graph is Hamiltonian is an NP-complete problem.For a graph G with vertex set V(G) and edge set E(G), the first Zagreb index (\(M_{1}\)) and second Zagreb index (\(M_{2}\)) are defined as \(M_{1}(G)=\sum \limits _{v_{i}v_{j}\in E(G)}(d_{G}(v_{i})+d_{G}(v_{j}))\)
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On scheduling multiple parallel two-stage flowshops with Johnson’s Rule J. Comb. Optim. (IF 1.0) Pub Date : 2024-02-23 Guangwei Wu, Fu Zuo, Feng Shi, Jianxin Wang
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EPTAS for parallel identical machine scheduling with time restrictions J. Comb. Optim. (IF 1.0) Pub Date : 2024-02-22 G. Jaykrishnan, Asaf Levin
We consider the non-preemptive scheduling problem on identical machines where there is a parameter B and each machine in every unit length time interval can process up to B different jobs. The goal function we consider is the makespan minimization and we develop an EPTAS for this problem. Prior to our work a PTAS was known only for the case of one machine and constant values of B, and even the case
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Hammock plots: visualizing categorical and numerical variables J. Comput. Graph. Stat. (IF 2.4) Pub Date : 2024-02-22 Matthias Schonlau
I discuss the hammock plot for visualizing categorical or mixed categorical/numeric data. Hammock plots can be viewed as a generalization of parallel coordinate plots where the lines are replaced b...
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An interpretable neural network-based non-proportional odds model for ordinal regression J. Comput. Graph. Stat. (IF 2.4) Pub Date : 2024-02-22 Akifumi Okuno, Kazuharu Harada
This study proposes an interpretable neural network-based non-proportional odds model (N3POM) for ordinal regression. N3POM is different from conventional approaches to ordinal regression with non-...
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The maximum number of copies of an even cycle in a planar graph J. Comb. Theory B (IF 1.4) Pub Date : 2024-02-22 Zequn Lv, Ervin Győri, Zhen He, Nika Salia, Casey Tompkins, Xiutao Zhu
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On the deepest cycle of a random mapping J. Comb. Theory A (IF 1.1) Pub Date : 2024-02-22 Ljuben Mutafchiev, Steven Finch
Let be the set of all mappings . The corresponding graph of is a union of disjoint connected unicyclic components. We assume that each is chosen uniformly at random (i.e., with probability ). The cycle of contained within its largest component is called the one. For any , let denote the length of this cycle. In this paper, we establish the convergence in distribution of and find the limits of its expectation
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Two conjectures of Andrews, Merca and Yee on truncated theta series J. Comb. Theory A (IF 1.1) Pub Date : 2024-02-22 Shane Chern, Ernest X.W. Xia
In their study of the truncation of Euler's pentagonal number theorem, Andrews and Merca introduced a partition function to count the number of partitions of in which is the least integer that is not a part and there are more parts exceeding than there are below . In recent years, two conjectures concerning on truncated theta series were posed by Andrews, Merca, and Yee. In this paper, we prove that
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Constructing generalized Heffter arrays via near alternating sign matrices J. Comb. Theory A (IF 1.1) Pub Date : 2024-02-21 L. Mella, T. Traetta
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How connectivity affects the extremal number of trees J. Comb. Theory B (IF 1.4) Pub Date : 2024-02-19 Suyun Jiang, Hong Liu, Nika Salia
The Erdős-Sós conjecture states that the maximum number of edges in an -vertex graph without a given -vertex tree is at most . Despite significant interest, the conjecture remains unsolved. Recently, Caro, Patkós, and Tuza considered this problem for host graphs that are connected. Settling a problem posed by them, for a -vertex tree , we construct -vertex connected graphs that are -free with at least
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Functional linear model with prior information of subjects’ network J. Comput. Graph. Stat. (IF 2.4) Pub Date : 2024-02-14 Xiaochen Zhang, Qingzhao Zhang, Kuangnan Fang
In many modern applications, data samples are interconnected by a network, and network information is a crucial factor in forecasting. However, existing network data analysis methods, which are des...
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Structured variational approximations with skew normal decomposable graphical models and implicit copulas J. Comput. Graph. Stat. (IF 2.4) Pub Date : 2024-02-14 Robert Salomone, Xuejun Yu, David J. Nott, Robert Kohn
Although there is much recent work developing flexible variational methods for Bayesian computation, Gaussian approximations with structured covariance matrices are often preferred computationally ...
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Nonparametric Additive Models for Billion Observations J. Comput. Graph. Stat. (IF 2.4) Pub Date : 2024-02-15 Mengyu Li, Jingyi Zhang, Cheng Meng
The nonparametric additive model (NAM) is a widely used nonparametric regression method. Nevertheless, due to the high computational burden, classic statistical techniques for fitting NAMs are not ...
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MFAI: A Scalable Bayesian Matrix Factorization Approach to Leveraging Auxiliary Information J. Comput. Graph. Stat. (IF 2.4) Pub Date : 2024-02-14 Zhiwei Wang, Fa Zhang, Cong Zheng, Xianghong Hu, Mingxuan Cai, Can Yang
In various practical situations, matrix factorization methods suffer from poor data quality, such as high data sparsity and low signal-to-noise ratio (SNR). Here, we consider a matrix factorization...
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Mixed Matrix Completion in Complex Survey Sampling under Heterogeneous Missingness* J. Comput. Graph. Stat. (IF 2.4) Pub Date : 2024-02-14 Xiaojun Mao, Hengfang Wang, Zhonglei Wang, Shu Yang
Modern surveys with large sample sizes and growing mixed-type questionnaires require robust and scalable analysis methods. In this work, we consider recovering a mixed dataframe matrix, obtained by...
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On the maximal number of elements pairwise generating the finite alternating group J. Comb. Theory A (IF 1.1) Pub Date : 2024-02-14 Francesco Fumagalli, Martino Garonzi, Pietro Gheri
Let be the alternating group of degree . Let be the maximal size of a subset of such that whenever and and let be the minimal size of a family of proper subgroups of whose union is . We prove that, when varies in the family of composite numbers, tends to 1 as . Moreover, we explicitly calculate for congruent to 3 modulo 18.
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On the packing number of antibalanced signed simple planar graphs of negative girth at least 5 J. Comb. Optim. (IF 1.0) Pub Date : 2024-02-12 Reza Naserasr, Weiqiang Yu
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On convexity in split graphs: complexity of Steiner tree and domination J. Comb. Optim. (IF 1.0) Pub Date : 2024-02-12 A. Mohanapriya, P. Renjith, N. Sadagopan
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An extension of the Christofides heuristic for a single-depot multiple Hamiltonian path problem J. Comb. Optim. (IF 1.0) Pub Date : 2024-02-12 Jun Wu, Zhen Yang, Guiqing Zhang, Yongxi Cheng
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Statistical inference in circular structural model and fitting circles to noisy data J. Comput. Graph. Stat. (IF 2.4) Pub Date : 2024-02-12 A. Donner, A. Goldenshluger
It is well known that commonly used algorithms for circle fitting perform poorly when sampling distribution of the points is not symmetric with respect to the circle center, e.g., when the points a...
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Improved shuffled Frog leaping algorithm with unsupervised population partitioning strategies for complex optimization problems J. Comb. Optim. (IF 1.0) Pub Date : 2024-02-11 Shikha Mehta
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A Q-polynomial structure for the Attenuated Space poset Aq(N,M) J. Comb. Theory A (IF 1.1) Pub Date : 2024-02-09 Paul Terwilliger
The goal of this article is to display a -polynomial structure for the Attenuated Space poset . The poset is briefly described as follows. Start with an -dimensional vector space over a finite field with elements. Fix an -dimensional subspace of . The vertex set of consists of the subspaces of that have zero intersection with . The partial order on is the inclusion relation. The -polynomial structure
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Most plane curves over finite fields are not blocking J. Comb. Theory A (IF 1.1) Pub Date : 2024-02-09 Shamil Asgarli, Dragos Ghioca, Chi Hoi Yip
A plane curve of degree is called if every -line in the plane meets at some -point. We prove that the proportion of blocking curves among those of degree is when and . We also show that the same conclusion holds for smooth curves under the somewhat weaker condition and . Moreover, the two events in which a random plane curve is smooth and respectively blocking are shown to be asymptotically independent