Polynomial Algorithm Research Articles

SAT solving for variants of first-order subsumption

AbstractAutomated reasoners, such as SAT/SMT solvers and first-order provers, are becoming the backbones of rigorous systems engineering, being used for example in applications of system verification, program synthesis, and cybersecurity. Automation in these domains crucially depends on the efficiency of the underlying reasoners towards finding proofs and/or counterexamples of the task to be enforced. In order to gain efficiency, automated reasoners use dedicated proof rules to keep proof search tractable. To this end, (variants of) subsumption is one of the most important proof rules used by automated reasoners, ranging from SAT solvers to first-order theorem provers and beyond. It is common that millions of subsumption checks are performed during proof search, necessitating efficient implementations. However, in contrast to propositional subsumption as used by SAT solvers and implemented using sophisticated polynomial algorithms, first-order subsumption in first-order theorem provers involves NP-complete search queries, turning the efficient use of first-order subsumption into a huge practical burden. In this paper we argue that the integration of a dedicated SAT solver opens up new venues for efficient implementations of first-order subsumption and related rules. We show that, by using a flexible learning approach to choose between various SAT encodings of subsumption variants, we greatly improve the scalability of first-order theorem proving. Our experimental results demonstrate that, by using a tailored SAT solver within first-order reasoning, we gain a large speedup in solving state-of-the-art benchmarks.

Edge‐arc‐disjoint paths in semicomplete mixed graphs

AbstractThe so‐called weak‐2‐linkage problem asks for a given digraph and distinct vertices of whether has arc‐disjoint paths so that is an ‐path for . This problem is NP‐complete for general digraphs but the first author showed that the problem is polynomially solvable and that all exceptions can be characterized when is a semicomplete digraph, that is, a digraph with no pair of nonadjacent vertices. In this paper we extend these results to paths which are both edge‐disjoint and arc‐disjoint in semicomplete mixed graphs, that is, a mixed graph in which every pair of distinct vertices has either an arc, an edge, or both an arc and an edge between them. We give a complete characterization of the negative instances and explain how this gives rise to a polynomial algorithm for the problem.

Fast Algorithm for Cyber-Attack Estimation and Attack Path Extraction Using Attack Graphs with AND/OR Nodes

This paper studies the security issues for cyber–physical systems, aimed at countering potential malicious cyber-attacks. The main focus is on solving the problem of extracting the most vulnerable attack path in a known attack graph, where an attack path is a sequence of steps that an attacker can take to compromise the underlying network. Determining an attacker’s possible attack path is critical to cyber defenders as it helps identify threats, harden the network, and thwart attacker’s intentions. We formulate this problem as a path-finding optimization problem with logical constraints represented by AND and OR nodes. We propose a new Dijkstra-type algorithm that combines elements from Dijkstra’s shortest path algorithm and the critical path method. Although the path extraction problem is generally NP-hard, for the studied special case, the proposed algorithm determines the optimal attack path in polynomial time, O(nm), where n is the number of nodes and m is the number of edges in the attack graph. To our knowledge this is the first exact polynomial algorithm that can solve the path extraction problem for different attack graphs, both cycle-containing and cycle-free. Computational experiments with real and synthetic data have shown that the proposed algorithm consistently and quickly finds optimal solutions to the problem.

State geometric adjustability for interval max‐plus linear systems

AbstractThis article investigates the state geometric adjustability for interval max‐plus linear systems, which means that the state vector sequence is transformed into a geometric vector sequence by using the state feedback control. It is pointed out that the geometric state vector sequence and its common ratio are closely related to the eigenvectors and eigenvalues of the special interval state matrix, respectively. Such an interval state matrix is determined by the eigen‐robust interval matrix, which has a universal eigenvector relative to a universal eigenvalue. The state geometric adjustability is characterized by the solvability of interval max‐plus linear equations, and a necessary and sufficient condition for the adjustability is given. A polynomial algorithm is provided to find the state feedback matrix. Several numerical examples and simulations are presented to demonstrate the results. At the same time, the proposed method is applied for the regulation of battery energy storage systems to optimize the start time of executing tasks for all processing units in each activity.

Elimination Algorithms for Skew Polynomials with Applications in Cybersecurity

It is evident that skew polynomials offer promising directions for developing cryptographic schemes. This paper focuses on exploring skew polynomials and studying their properties with the aim of exploring their potential applications in fields such as cryptography and combinatorics. We begin by deriving the concept of resultants for bivariate skew polynomials. Then, we employ the derived resultant to incrementally eliminate indeterminates in skew polynomial systems, utilising both direct and modular approaches. Finally, we discuss some applications of the derived resultant, including cryptographic schemes (such as Diffie–Hellman) and combinatorial identities (such as Pascal’s identity). We start by considering a bivariate skew polynomial system with two indeterminates; our intention is to isolate and eliminate one of the indeterminates to reduce the system to a simpler form (that is, relying only on one indeterminate in this case). The methodology is composed of two main techniques; in the first technique, we apply our definition of a (bivariate) resultant via a Sylvester-style matrix directly from the polynomials’ coefficients, while the second is based on modular methods where we compute the resultant by using evaluation and interpolation approaches. The idea of this second technique is that instead of computing the resultant directly from the coefficients, we propose to evaluate the polynomials at a set of valid points to compute its corresponding set of partial resultants first; then, we can deduce the original resultant by combining all these partial resultants using an interpolation technique by utilising a theorem we have established.

A Kernel-Based Calibration Algorithm for Chromatic Confocal Line Sensors.

In chromatic confocal line sensors, calibration is usually divided into peak extraction and wavelength calibration. In previous research, the focus was mainly on peak extraction. In this paper, a kernel-based algorithm is proposed to deal with wavelength calibration, which corresponds to the mapping relationship between peaks (i.e., the wavelengths) in image space and profiles in physical space. The primary component of the mapping function is depicted using polynomial basis functions, which are distinguished along various dispersion axes. Considering the unknown distortions resulting from field curvature, sensor fabrication and assembly, and even the inherent complexity of dispersion, a typical kernel trick-based nonparametric function element is introduced here, predicated on the notion that similar processes conducted on the same sensor yield comparable distortions.To ascertain the performance with and without the kernel trick, we carried out wavelength calibration and groove fitting on a standard groove sample processed via glass grinding and with a reference depth of 66.14 μm. The experimental results show that depths calculated by the kernel-based calibration algorithm have higher accuracy and lower uncertainty than those ascertained using the conventional polynomial algorithm. As such, this indicates that the proposed algorithm provides effective improvements.

Real‐time estimation of olive flounder growth in indoor aquaculture using cameras combined with a grid

AbstractEstimating fish growth in real time has many benefits for indoor aquaculture farms, such as saving labor time and costs, reducing water pollution during feeding, improving feeding activity and determining when to harvest. Hence, this study proposed a visual‐information‐based method for measuring olive flounder (Paralichthys olivaceus) length, using accurate growth tracking for efficient aquaculture management. Using two cameras, an light‐emitting‐diode (LED) grid was placed at the bottom of the water tank to measure fish length. The pixels unit from the fish length in the captured image was converted to centimeters based on the relationship of a pre‐built dataset. A total of 180 lengths were calculated using images captured by the cameras. The average length of each fish acquired from the cameras was calculated separately, and Lagrange's interpolating polynomial algorithm was implemented to calculate the overall length of each fish. This method reduced the computational complexity, and results were obtained more rapidly and in a user‐friendly environment. The power model generated the length–weight relationship, which allowed us to estimate the body weight of each olive flounder based on the length. The proposed approach enabled us to calculate the length of olive flounders with a highly accurate R2 of 0.995.

Satellite retrieval of oceanic particulate organic carbon: Towards an accurate and seamless dataset for the global ocean

Particulate organic carbon (POC) plays crucial roles in the global ocean carbon cycle and the oceanic biological pump. Satellite remote sensing has been demonstrated to be an effective technique for the retrieval of surface oceanic POC concentration. However, the complex spatiotemporal variations of the relationships between POC and oceanic optical properties across different waters posed challenges for accurate retrieval of POC concentration from satellite observations. Additionally, interference factors, such as cloud cover and sun glint, resulted in severe data missing problems and impeding daily coverage of the global ocean. With an attempt to generate accurate, seamless and readily available POC products for the global ocean, this study aimed to develop accurate satellite POC retrieval models for the Moderate Resolution Imaging Spectroradiometer (MODIS) data from both Terra and Aqua satellites, and to explore the possibility of using the empirical orthogonal function interpolation technique (DINEOF) to reconstruct satellite-retrieved POC data to generate gap-free global oceanic POC products. Results showed that the eXtreme Gradient Boosting (XGBoost) method could accurately retrieve POC with R2 approximately 0.80 and RMSE about 0.20 in log10 scale, obviously outperforming the operational blue-to-green band ratio algorithm and the hybrid polynomial algorithm based on two multi-band indices; and the DINEOF method, which could reconstruct approximately 88 % missing pixels for the global ocean, contributed to better revealing the global oceanic POC variations at a daily scale than the satellite-retrieved POC products. Based on the developed models, a suit of long time-series accurate and seamless POC products of the global surface ocean were generated, which is readily available for other applications and should be helpful to investigate the spatiotemporal variations of POC concentrations over global ocean and its roles in the global carbon cycle. The generated seamless products are openly accessible via the DOIs listed in the data availability section.

Connected and Autonomous Vehicle Scheduling Problems: Some Models and Algorithms

In this paper, we consider some problems that arise in connected and autonomous vehicle (CAV) systems. Their simplified variants can be formulated as scheduling problems. Therefore, scheduling solution algorithms can be used as a part of solution algorithms for real-world problems. For four variants of such problems, mathematical models and solution algorithms are presented. In particular, three polynomial algorithms and a branch and bound algorithm are developed. These CAV scheduling problems are considered in the literature for the first time. More complicated NP-hard scheduling problems related to CAVs can be considered in the future.

MPSDynamics.jl: Tensor network simulations for finite-temperature (non-Markovian) open quantum system dynamics.

The MPSDynamics.jl package provides an easy-to-use interface for performing open quantum systems simulations at zero and finite temperatures. The package has been developed with the aim of studying non-Markovian open system dynamics using the state-of-the-art numerically exact Thermalized-Time Evolving Density operator with Orthonormal Polynomials Algorithm based on environment chain mapping. The simulations rely on a tensor network representation of the quantum states as matrix product states (MPS) and tree tensor network states. Written in the Julia programming language, MPSDynamics.jl is a versatile open-source package providing a choice of several variants of the Time-Dependent Variational Principle method for time evolution (including novel bond-adaptive one-site algorithms). The package also provides strong support for the measurement of single and multi-site observables, as well as the storing and logging of data, which makes it a useful tool for the study of many-body physics. It currently handles long-range interactions, time-dependent Hamiltonians, multiple environments, bosonic and fermionic environments, and joint system-environment observables.

Multithreading-Based Algorithm for High-Performance Tchebichef Polynomials with Higher Orders

Tchebichef polynomials (TPs) play a crucial role in various fields of mathematics and applied sciences, including numerical analysis, image and signal processing, and computer vision. This is due to the unique properties of the TPs and their remarkable performance. Nowadays, the demand for high-quality images (2D signals) is increasing and is expected to continue growing. The processing of these signals requires the generation of accurate and fast polynomials. The existing algorithms generate the TPs sequentially, and this is considered as computationally costly for high-order and larger-sized polynomials. To this end, we present a new efficient solution to overcome the limitation of sequential algorithms. The presented algorithm uses the parallel processing paradigm to leverage the computation cost. This is performed by utilizing the multicore and multithreading features of a CPU. The implementation of multithreaded algorithms for computing TP coefficients segments the computations into sub-tasks. These sub-tasks are executed concurrently on several threads across the available cores. The performance of the multithreaded algorithm is evaluated on various TP sizes, which demonstrates a significant improvement in computation time. Furthermore, a selection for the appropriate number of threads for the proposed algorithm is introduced. The results reveal that the proposed algorithm enhances the computation performance to provide a quick, steady, and accurate computation of the TP coefficients, making it a practical solution for different applications.

Efficient estimation of the modified Gromov–Hausdorff distance between unweighted graphs

Gromov–Hausdorff distances measure shape difference between the objects representable as compact metric spaces, e.g. point clouds, manifolds, or graphs. Computing any Gromov–Hausdorff distance is equivalent to solving an NP-hard optimization problem, deeming the notion impractical for applications. In this paper we propose a polynomial algorithm for estimating the so-called modified Gromov–Hausdorff (mGH) distance, a relaxation of the standard Gromov–Hausdorff (GH) distance with similar topological properties. We implement the algorithm for the case of compact metric spaces induced by unweighted graphs as part of Python library scikit-tda, and demonstrate its performance on real-world and synthetic networks. The algorithm finds the mGH distances exactly on most graphs with the scale-free property. We use the computed mGH distances to successfully detect outliers in real-world social and computer networks.

An Efficient Algorithm to Compute the Toughness in Graphs with Bounded Treewidth

Let t be a positive real number. A graph is called t-tough if the removal of any vertex set S that disconnects the graph leaves at most |S|/t components. The toughness of a graph is the largest t for which the graph is t-tough. We prove that toughness is fixed-parameter tractable parameterized with the treewidth. More precisely, we give an algorithm to compute the toughness of a graph G with running time O(|V(G)|3·tw(G)2tw(G))\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\mathcal {O}}(|V(G)|^3\\cdot \ extrm{tw}(G)^{2\ extrm{tw}(G)})$$\\end{document} where tw(G)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ extrm{tw}(G)$$\\end{document} is the treewidth. If the treewidth is bounded by a constant, then this is a polynomial algorithm.

An Efficient Algorithm for Solving the 2-MAXSAT Problem

In the maximum satisfiability (MAXSAT) problem, we are given a set V of m variables and a collection C of n clauses over V. We will seek a truth assignment to maximize the number of satisfied clauses. This problem is NP-hard even for its restricted version, the 2-MAXSAT problem, in which every clause contains at most two literals. In this paper, we discuss an efficient algorithm to solve this problem. Its worst-case time complexity is bounded by . In the case that log2 nm is bounded by a constant, our algorithm is a polynomial algorithm. In terms of Garey and Johnson, any satisfiability instance can be transformed to a 2-MAXSAT instance in polynomial time. Thus, our algorithm may lead to a proof of P = NP.

Polynomial crisp-minimization algorithm for fuzzy deterministic automata

Designing automata minimization algorithms is a significant topic in Automata Theory and Languages with practical applications. In this paper, we develop an efficient minimization algorithm for deterministic fuzzy finite automata over locally finite lattices. More precisely, the algorithm outputs an equivalent minimal crisp-deterministic fuzzy finite automaton for an input fuzzy deterministic finite automaton (FDfA). The running time of the proposed algorithm is polynomial for particular types of locally finite lattices, specifically for max-min-based complete residuated lattices. The intuition behind the proposed algorithm relies on the polynomial minimization algorithm's intuition for ordinary deterministic automata developed by Vazquez de Parga, Garcia, and Lopez (2013) [35]. The motivation for this study comes from the fact that the original algorithm's notions and meanings are lost in the context of fuzzy automata. Thus, we establish a new theoretical foundation that provides the correctness and the polynomial-time nature of this new crisp-minimization algorithm for FDfAs.

Exploring percolation features with polynomial algorithms for classifying Covid-19 in chest X-ray images

Covid-19 is a severe illness caused by the Sars-CoV-2 virus, initially identified in China in late 2019 and swiftly spreading globally. Since the virus primarily impacts the lungs, analyzing chest X-rays stands as a reliable and widely accessible means of diagnosing the infection. In computer vision, deep learning models such as CNNs have been the main adopted approach for detection of Covid-19 in chest X-ray images. However, we believe that handcrafted features can also provide relevant results, as shown previously in similar image classification challenges. In this study, we propose a method for identifying Covid-19 in chest X-ray images by extracting and classifying local and global percolation-based features. This technique was tested on three datasets: one comprising 2,002 segmented samples categorized into two groups (Covid-19 and Healthy); another with 1,125 non-segmented samples categorized into three groups (Covid-19, Healthy, and Pneumonia); and a third one composed of 4,809 non-segmented images representing three classes (Covid-19, Healthy, and Pneumonia). Then, 48 percolation features were extracted and give as input into six distinct classifiers. Subsequently, the AUC and accuracy metrics were assessed. We used the 10-fold cross-validation approach and evaluated lesion sub-types via binary and multiclass classification using the Hermite polynomial classifier, a novel approach in this domain. The Hermite polynomial classifier exhibited the most promising outcomes compared to five other machine learning algorithms, wherein the best obtained values for accuracy and AUC were 98.72% and 0.9917, respectively. We also evaluated the influence of noise in the features and in the classification accuracy. These results, based in the integration of percolation features with the Hermite polynomial, hold the potential for enhancing lesion detection and supporting clinicians in their diagnostic endeavors.

A network flow approach to a common generalization of Clar and Fries numbers

Clar number and Fries number are two thoroughly investigated parameters of plane graphs emerging from mathematical chemistry to measure stability of organic molecules. First, we introduce a common generalization of these two concepts for bipartite plane graphs, and then we extend it further to the notion of source-sink pairs of subsets of nodes in a general (not necessarily planar) directed graph. The main result is a min-max formula for the maximum weight of a source-sink pair. The proof is based on the recognition that the convex hull of source-sink pairs can be obtained as the projection of a network tension polyhedron. The construction makes it possible to apply any standard cheapest network flow algorithm to compute both a maximum weight source-sink pair and a minimizer of the dual optimization problem formulated in the min-max theorem. As a consequence, our approach gives rise to the first purely combinatorial, strongly polynomial algorithm to compute a largest (or even a maximum weight) Fries-set of a perfectly matchable plane bipartite graph and an optimal solution to the dual minimization problem.

Managing temperature in open quantum systems strongly coupled with structured environments.

In non-perturbative non-Markovian open quantum systems, reaching either low temperatures with the hierarchical equations of motion (HEOM) or high temperatures with the Thermalized Time Evolving Density Operator with Orthogonal Polynomials Algorithm (T-TEDOPA) formalism in Hilbert space remains challenging. We compare different ways of modeling the environment. Sampling the Fourier transform of the bath correlation function, also called temperature dependent spectral density, proves to be very effective. T-TEDOPA [Tamascelli et al., Phys. Rev. Lett. 123, 090402 (2019)] uses a linear chain of oscillators with positive and negative frequencies, while HEOM is based on the complex poles of an optimized rational decomposition of the temperature dependent spectral density [Xu et al., Phys. Rev. Lett. 129, 230601 (2022)]. Resorting to the poles of the temperature independent spectral density and of the Bose function separately is an alternative when the problem due to the huge number of Bose poles at low temperatures is circumvented. Two examples illustrate the effectiveness of the HEOM and T-TEDOPA approaches: a benchmark pure dephasing case and a two-bath model simulating the dynamics of excited electronic states coupled through a conical intersection. We show the efficiency of T-TEDOPA to simulate dynamics at a finite temperature by using either continuous spectral densities or only all the intramolecular oscillators of a linear vibronic model calibrated from abinitio data of a phenylene ethynylene dimer.

Non-numerical weakly relational domains

The weakly relational domain of Octagons offers a decent compromise between precision and efficiency for numerical properties. Here, we are concerned with the construction of non-numerical relational domains. We provide a general construction of weakly relational domains, which we exemplify with an extension of constant propagation by disjunctions. Since for the resulting domain of 2-disjunctive formulas satisfiability is NP-complete, we provide a general construction for a further, more abstract, weakly relational domain where the abstract operations of restriction and least upper bound can be efficiently implemented. In the second step, we consider a relational domain that tracks conjunctions of inequalities between variables, and between variables and constants for arbitrary partial orders of values. Examples are sub(multi)sets, as well as prefix, substring or scattered substring orderings on strings. When the partial order is a lattice, we provide precise polynomial algorithms for satisfiability, restriction, and the best abstraction of disjunction. Complementary to the constructions for lattices, we find that, in general, satisfiability of conjunctions is NP-complete. We therefore again provide polynomial abstract versions of restriction, conjunction, and join. By using our generic constructions, these domains are extended to weakly relational domains that additionally track disjunctions. For all our domains, we indicate how abstract transformers for assignments and guards can be constructed.

Structure of some ( [formula omitted], [formula omitted])-free graphs with application to colorings

We use Pt and Ct to denote a path and a cycle on t vertices, respectively. A diamond is a graph obtained from two triangles that share exactly one edge. A kite is a graph that consists of a diamond and another vertex adjacent to a vertex of degree 2 of the diamond. A gem is a graph that consists of a P4 plus a vertex adjacent to all vertices of the P4. Choudum et al. (2021) proved that every (P7,C7,C4, gem)-free graph has either a clique cutset or a vertex whose neighbors is the union of two cliques unless it is a clique blowup of the Petersen graph, and every non-Petersen (P7,C7,C4, diamond)-free graph has either a clique cutset or has a vertex of degree at most max{2,ω(G)−1}. In this paper, we extend these two results respectively to (P7,C4, gem)-free graphs and (P7,C4, diamond)-free graphs. We also prove that if G is a (P7,C4, kite)-free graph with δ(G)≤ω(G) and without clique cutsets, then there is an integer ℓ≥0 such that G=Kℓ+H, where H is either the Petersen graph or a well-defined graph F. This implies a polynomial algorithm which can determine the chromatic number of (P7,C4, diamond)-free graphs, color (P7,C4, kite)-free graphs G with (ω(G)+1) colors, and color (P7,C4, gem)-free graphs G with (2ω(G)−1) colors.