Tries-Based Parallel Solutions for Generating Perfect Crosswords Grids

A general crossword grid generation is considered an NP-complete problem and theoretically it could be a good candidate to be used by cryptography algorithms. In this article, we propose a new algorithm for generating perfect crosswords grids (with no black boxes) that relies on using tries data structures, which are very important for reducing the time for finding the solutions, and offers good opportunity for parallelisation, too. The algorithm uses a special tries representation and it is very efficient, but through parallelisation the performance is improved to a level that allows the solution to be obtained extremely fast. The experiments were conducted using a dictionary of almost 700,000 words, and the solutions were obtained using the parallelised version with an execution time in the order of minutes. We demonstrate here that finding a perfect crossword grid could be solved faster than has been estimated before, if we use tries as supporting data structures together with parallelisation. Still, if the size of the dictionary is increased by a lot (e.g., considering a set of dictionaries for different languages—not only for one), or through a generalisation to a 3D space or multidimensional spaces, then the problem still could be investigated for a possible usage in cryptography.

Polynomial time solution to NP-complete problem Hamiltonian cycle (P=NP Proof)

P vs NP is one of the open and most important mathematics/computer science questions that has not been answered since it was raised in 1971 despite its importance and a quest for a solution since 2000. P vs NP is a class of problems that no polynomial time algorithm exists for any. If any of the problems in the class gets solved in polynomial time, all can be solved as the problems are translatable to each other. One of the famous problems of this kind is Hamiltonian cycle. Here we propose a polynomial time algorithm with rigorous proof that it always finds a solution if there exists one. It is expected that this solution would address all problems in the class and have a major impact in diverse fields including computer science, engineering, biology, and cryptography.

Hybrid Scheduling Strategy in Cloud Computing based on Optimization Algorithms

As a platform for offering on-demand services, cloud computing has increased in relevance and appeal. It has a pay-per-use model for its services. A cloud service provider's primary goal is to efficiently use resources by reducing execution time, cost, and other factors while increasing profit. As a result, effective scheduling algorithms remain a key issue in cloud computing, and this topic is categorized as an NP-complete problem. Researchers previously proposed several optimization techniques to address the NP-complete problem, but more work is needed in this area. This paper provides an overview of strategy for successful task scheduling based on a hybrid heuristic approach for both regular and larger workloads. The previous method handles the jobs adequately, but its performance degrades as the task size becomes larger. The proposed optimum scheduling method employs two distinct techniques to select the suitable VM for the specified job. To begin, it enhances the LJFP method by employing OSIG, an upgraded version of the Genetic Algorithm, to choose solutions with improved fitness factors, crossover, and mutation operators. This selection returns the best machines, and PSO then chooses one for a specific job. The appropriate machine is chosen depending on several factors, including the expected execution time, current load, and energy usage. The proposed algorithm's performance is assessed using two distinct cloud scenarios with various VMs and tasks, and overall execution time and energy usage are calculated. The proposed algorithm outperforms existing techniques in terms of energy and average execution time usage in both scenarios.

Computational complexity of the word problem in modal algebras

Приводится доказательство $\mathrm{PSPACE}$-полноты проблемы равенства слов в классе всех нуль-порождённых модальных алгебр, или, эквивалентно, проблемы равенства константных слов в классе всех модальных алгебр. Также рассматривается вопрос о сложности равенства слов в произвольном многообразии модальных алгебр. Доказывается, что уже проблема равенства константных слов в многообразии модальных алгебр может быть сколь угодно трудной (включая как классы сложности, так и степени неразрешимости). Показано, как построить соответствующие многообразия. The paper deals with the word problem for modal algebras. It is proved that, for the variety of all modal algebras, the word problem is $\mathrm{PSPACE}$-complete if only constant modal terms or only $0$-generated modal algebras are considered. We also consider the word problem for different varieties of modal algebras. It is proved that the word problem for a variety of modal algebras can be $C$-hard, for any complexity class or unsolvability degree $C$ containing a $C$-complete problem. It is shown how to construct such varieties.

On Independent Secondary Dominating Sets in Generalized Graph Products

In 2008, Hedetniemi et al. introduced (1,k)-domination in graphs. The research on this concept was extended to the problem of existence of independent (1,k)-dominating sets, which is an NP-complete problem. In this paper, we consider independent (1,1)- and (1,2)-dominating sets, which we name as (1,1)-kernels and (1,2)-kernels, respectively. We obtain a complete characterization of generalized corona of graphs and G-join of graphs, which have such kernels. Moreover, we determine some graph parameters related to these sets, such as the number and the cardinality. In general, graph products considered in this paper have an asymmetric structure, contrary to other many well-known graph products (Cartesian, tensor, strong).

P Systems with Evolutional Communication and Division Rules

A widely studied field in the framework of membrane computing is computational complexity theory. While some types of P systems are only capable of efficiently solving problems from the class P, adding one or more syntactic or semantic ingredients to these membrane systems can give them the ability to efficiently solve presumably intractable problems. These ingredients are called to form a frontier of efficiency, in the sense that passing from the first type of P systems to the second type leads to passing from non-efficiency to the presumed efficiency. In this work, a solution to the SAT problem, a well-known NP-complete problem, is obtained by means of a family of recognizer P systems with evolutional symport/antiport rules of length at most (2,1) and division rules where the environment plays a passive role; that is, P systems from CDEC^(2,1). This result is comparable to the one obtained in the tissue-like counterpart, and gives a glance of a parallelism and the non-evolutionary membrane systems with symport/antiport rules.

Computing cliques and cavities in networks

Complex networks contain complete subgraphs such as nodes, edges, triangles, etc., referred to as simplices and cliques of different orders. Notably, cavities consisting of higher-order cliques play an important role in brain functions. Since searching for maximum cliques is an NP-complete problem, we use k-core decomposition to determine the computability of a given network. For a computable network, we design a search method with an implementable algorithm for finding cliques of different orders, obtaining also the Euler characteristic number. Then, we compute the Betti numbers by using the ranks of boundary matrices of adjacent cliques. Furthermore, we design an optimized algorithm for finding cavities of different orders. Finally, we apply the algorithm to the neuronal network of C. elegans with data from one typical dataset, and find all of its cliques and some cavities of different orders, providing a basis for further mathematical analysis and computation of its structure and function.

Matroids, Complexity and Computation

<p>The node deletion problem on graphs is: given a graph and integer k, can we delete no more than k vertices to obtain a graph that satisfies some property π. Yannakakis showed that this problem is NP-complete for an infinite family of well- defined properties. The edge deletion problem and matroid deletion problem are similar problems where given a graph or matroid respectively, we are asked if we can delete no more than k edges/elements to obtain a graph/matroid that satisfies a property π. We show that these problems are NP-hard for similar well-defined infinite families of properties. In 1991 Vertigan showed that it is #P-complete to count the number of bases of a representable matroid over any fixed field. However no publication has been produced. We consider this problem and show that it is #P-complete to count the number of bases of matroids representable over any infinite fixed field or finite fields of a fixed characteristic. There are many different ways of describing a matroid. Not all of these are polynomially equivalent. That is, given one description of a matroid, we cannot create another description for the same matroid in time polynomial in the size of the first description. Due to this, the complexity of matroid problems can vary greatly depending on the method of description used. Given one description a problem might be in P while another description gives an NP-complete problem. Based on these interactions between descriptions, we create and study the hierarchy of all matroid descriptions and generalize this to all descriptions of countable objects.</p>

Matroids, Complexity and Computation

<p>The node deletion problem on graphs is: given a graph and integer k, can we delete no more than k vertices to obtain a graph that satisfies some property π. Yannakakis showed that this problem is NP-complete for an infinite family of well- defined properties. The edge deletion problem and matroid deletion problem are similar problems where given a graph or matroid respectively, we are asked if we can delete no more than k edges/elements to obtain a graph/matroid that satisfies a property π. We show that these problems are NP-hard for similar well-defined infinite families of properties. In 1991 Vertigan showed that it is #P-complete to count the number of bases of a representable matroid over any fixed field. However no publication has been produced. We consider this problem and show that it is #P-complete to count the number of bases of matroids representable over any infinite fixed field or finite fields of a fixed characteristic. There are many different ways of describing a matroid. Not all of these are polynomially equivalent. That is, given one description of a matroid, we cannot create another description for the same matroid in time polynomial in the size of the first description. Due to this, the complexity of matroid problems can vary greatly depending on the method of description used. Given one description a problem might be in P while another description gives an NP-complete problem. Based on these interactions between descriptions, we create and study the hierarchy of all matroid descriptions and generalize this to all descriptions of countable objects.</p>

As good as it gets: A scaling comparison of DNA computing, network biocomputing, and electronic computing approaches to an NP-complete problem

All known algorithms to solve Nondeterministic Polynomial (NP) Complete problems, relevant to many real-life applications, require the exploration of a space of potential solutions, which grows exponentially with the size of the problem. Since electronic computers can implement only limited parallelism, their use for solving NP-complete problems is impractical for very large instances, and consequently alternative massively parallel computing approaches were proposed to address this challenge. We present a scaling analysis of two such alternative computing approaches, DNA Computing (DNA-C) and Network Biocomputing with Agents (NB-C), compared with Electronic Computing (E-C). The Subset Sum Problem (SSP), a known NP-complete problem, was used as a computational benchmark, to compare the volume, the computing time, and the energy required for each type of computation, relative to the input size. Our analysis shows that the sequentiality of E-C translates in a very small volume compared to that required by DNA-C and NB-C, at the cost of the E-C computing time being outperformed first by DNA-C (linear run time), followed by NB-C. Finally, NB-C appears to be more energy-efficient than DNA-C for some types of input sets, while being less energy-efficient for others, with E-C being always an order of magnitude less energy efficient than DNA-C. This scaling study suggest that presently none of these computing approaches win, even theoretically, for all three key performance criteria, and that all require breakthroughs to overcome their limitations, with potential solutions including hybrid computing approaches.