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Journal articles on the topic 'Computationally hard problems'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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1

Ashok, B., and T. K. Patra. "Locating phase transitions in computationally hard problems." Pramana 75, no. 3 (2010): 549–63. http://dx.doi.org/10.1007/s12043-010-0138-0.

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Schleitzer, Agnes, and Olaf Beyersdorff. "Computationally Hard Problems Are Hard for QBF Proof Systems Too." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 11 (2025): 11336–44. https://doi.org/10.1609/aaai.v39i11.33233.

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There has been tremendous progress in the past decade in the field of quantified Boolean formulas (QBF), both in practical solving as well as in creating a theory of corresponding proof systems and their proof complexity analysis. Both for solving and for proof complexity, it is important to have interesting formula families on which we can test solvers and gauge the strength of the proof systems. There are currently few such formula families in the literature. We initiate a general programme how to transform computationally hard problems (located in the polynomial hierarchy) into QBFs hard fo

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Chiu, D. T., E. Pezzoli, H. Wu, A. D. Stroock, and G. M. Whitesides. "Using three-dimensional microfluidic networks for solving computationally hard problems." Proceedings of the National Academy of Sciences 98, no. 6 (2001): 2961–66. http://dx.doi.org/10.1073/pnas.061014198.

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Žerovnik, Janez. "Heuristics for NP-hard optimization problems - simpler is better!?" Logistics & Sustainable Transport 6, no. 1 (2015): 1–10. http://dx.doi.org/10.1515/jlst-2015-0006.

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Abstract We provide several examples showing that local search, the most basic metaheuristics, may be a very competitive choice for solving computationally hard optimization problems. In addition, generation of starting solutions by greedy heuristics should be at least considered as one of very natural possibilities. In this critical survey, selected examples discussed include the traveling salesman, the resource-constrained project scheduling, the channel assignment, and computation of bounds for the Shannon capacity.

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Zaikin, Oleg, Pavel Petrov, Mikhail Posypkin, Vadim Bulavintsev, and Ilya Kurochkin. "A Volunteer Computing Project for Solving Geoacoustic Inversion Problems." Open Engineering 7, no. 1 (2017): 363–70. http://dx.doi.org/10.1515/eng-2017-0040.

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AbstractA volunteer computing project aimed at solving computationally hard inverse problems in underwater acoustics is described. This project was used to study the possibilities of the sound speed profile reconstruction in a shallow-water waveguide using a dispersion-based geoacoustic inversion scheme. The computational capabilities provided by the project allowed us to investigate the accuracy of the inversion for different mesh sizes of the sound speed profile discretization grid. This problem suits well for volunteer computing because it can be easily decomposed into independent simpler s

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Dean, Walter. "Computational Complexity Theory and the Philosophy of Mathematics†." Philosophia Mathematica 27, no. 3 (2019): 381–439. http://dx.doi.org/10.1093/philmat/nkz021.

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Abstract Computational complexity theory is a subfield of computer science originating in computability theory and the study of algorithms for solving practical mathematical problems. Amongst its aims is classifying problems by their degree of difficulty — i.e., how hard they are to solve computationally. This paper highlights the significance of complexity theory relative to questions traditionally asked by philosophers of mathematics while also attempting to isolate some new ones — e.g., about the notion of feasibility in mathematics, the $\mathbf{P} \neq \mathbf{NP}$ problem and why it has

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Lauri, Juho, and Sourav Dutta. "Fine-Grained Search Space Classification for Hard Enumeration Variants of Subset Problems." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 2314–21. http://dx.doi.org/10.1609/aaai.v33i01.33012314.

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We propose a simple, powerful, and flexible machine learning framework for (i) reducing the search space of computationally difficult enumeration variants of subset problems and (ii) augmenting existing state-of-the-art solvers with informative cues arising from the input distribution. We instantiate our framework for the problem of listing all maximum cliques in a graph, a central problem in network analysis, data mining, and computational biology. We demonstrate the practicality of our approach on real-world networks with millions of vertices and edges by not only retaining all optimal solut

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DANTSIN, EVGENY, and ALEXANDER WOLPERT. "A ROBUST DNA COMPUTATION MODEL THAT CAPTURES PSPACE." International Journal of Foundations of Computer Science 14, no. 05 (2003): 933–51. http://dx.doi.org/10.1142/s0129054103002096.

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One of the most serious problems in DNA computing is that basic DNA operations are faulty. Many DNA computation models use operations based on annealing and magnetic-beads separation which sometimes produce undesirable results. Some models use reliable operations only but their computational power is too weak. The purpose of this paper is to find a good trade-off between the robustness of DNA operations and their computational power. We present a robust DNA computation model that can solve computationally hard problems. We prove that (i) this model can solve PSPACE-complete problems, and (ii)

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LIU, JIMING, XIAOLONG JIN, and JING HAN. "DISTRIBUTED PROBLEM SOLVING WITHOUT COMMUNICATION — AN EXAMINATION OF COMPUTATIONALLY HARD SATISFIABILITY PROBLEMS." International Journal of Pattern Recognition and Artificial Intelligence 16, no. 08 (2002): 1041–64. http://dx.doi.org/10.1142/s0218001402002143.

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In this paper, we extend and modify the ERA approach proposed in Ref. 13 to solve Propositional Satisfiability Problems (SATs). The new ERA approach involves a multiagent system where each agent only senses its local environment and applies some self-organizing rules for governing its movements. The environment, which is a two-dimensional cellular environment, records and updates the local values that are computed and affected according to the movements of individual agents. In solving a SAT with the ERA approach, we first divide variables into several groups, and represent each variable group

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Kel’manov, A. V., and S. M. Romanchenko. "Pseudopolynomial algorithms for certain computationally hard vector subset and cluster analysis problems." Automation and Remote Control 73, no. 2 (2012): 349–54. http://dx.doi.org/10.1134/s0005117912020129.

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11

Kumar, Santosh, and Elias Munapo. "Innovative Ways of Developing and Using Specific Purpose Alternatives for Solving Hard Combinatorial Network Routing and Ordered Optimisation Problems." AppliedMath 4, no. 2 (2024): 791–805. http://dx.doi.org/10.3390/appliedmath4020042.

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This paper reviews some recent contributions by the authors and their associates and highlights a few innovative ideas, which led them to address some hard combinatorial network routing and ordered optimisation problems. The travelling salesman, which is in the NP hard category, has been reviewed and solved as an index-restricted shortest connected graph, and therefore, it opens a question about its ‘NP Hard’ category. The routing problem through ‘K’ specified nodes and ordered optimum solutions are computationally demanding but have been made computationally feasible. All these approaches are

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12

Chitnis, Rajesh, MohammadTaghi Hajiaghayi, and Vahid Liaghat. "Parameterized Complexity of Problems in Coalitional Resource Games." Proceedings of the AAAI Conference on Artificial Intelligence 25, no. 1 (2011): 620–25. http://dx.doi.org/10.1609/aaai.v25i1.7887.

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Coalition formation is a key topic in multi-agent systems. Coalitions enable agents to achieve goals that they may nothave been able to achieve on their own. Previous work hasshown problems in coalition games to be computationally hard. Wooldridge and Dunne (Artifi. Intell. 2006) studied the classical computational complexity of several natural decision problems in Coalitional Resource Games (CRG) - games in which each agent is endowed with a set of resources and coalitions can bring about a set of goals if they are collectively endowed with the necessary amount of resources. The input of coal

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13

Yazdani Sabouni, M. T., and Rasaratnam Logendran. "A Simplified Branch-and-Price Mechanism for a Three-Machine Dynamic PCB Assembly." Applied Mechanics and Materials 598 (July 2014): 398–403. http://dx.doi.org/10.4028/www.scientific.net/amm.598.398.

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A dynamic Printed Circuit Board (PCB) assembly system along with the most complete form of setup time in this assembly is introduced in this paper. An effective Branch-and-Price mechanism is employed to develop a lower bound for the three-machine problem. However, solving the sub problems remains computationally difficult because of their NP-hard nature. An optimal approach is established, which makes solving the sub problems computationally feasible.

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Gao, Xi, and Hai Zhu Chen. "Signed Integer Arithmetic on Spiking Neural P System." Applied Mechanics and Materials 20-23 (January 2010): 779–84. http://dx.doi.org/10.4028/www.scientific.net/amm.20-23.779.

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Spiking neural P systems are a new computing model incorporating the ideas of spiking neurons into membrane computing. It has been shown that they have powerful computational capability and potential capability in solving computationally hard problems. The paper describes the implementation of signed integer arithmetic operations on spiking neural P system with the inputs and outputs encoded as appropriate sequences of spikes. The present work may be considered as the basis for more complex applications, maybe for constructing computer chips or for solving general mathematical problems.

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LEE, OukJae, and Seokmin HONG. "Probabilistic Computing Based on Random Devices." Physics and High Technology 32, no. 11 (2023): 3–9. http://dx.doi.org/10.3938/phit.32.028.

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Recently, a new type of computing called as probabilistic computing approach has shown its broad application in various problems ranging from combinatorial optimizations and machine learning to quantum simulation where a randomly fluctuating bit (p-bit) constitutes a basic building block. This computing scheme tackles computationally hard problems in specific domains that can be efficiently solved using probabilistic algorithms compared to conventional deterministic computers. Here, we broadly review several aspects of this computing scheme starting from its building blocks and system network

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Lancia, Giuseppe, and Paolo Serafini. "Computational Complexity and ILP Models for Pattern Problems in the Logical Analysis of Data." Algorithms 14, no. 8 (2021): 235. http://dx.doi.org/10.3390/a14080235.

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Logical Analysis of Data is a procedure aimed at identifying relevant features in data sets with both positive and negative samples. The goal is to build Boolean formulas, represented by strings over {0,1,-} called patterns, which can be used to classify new samples as positive or negative. Since a data set can be explained in alternative ways, many computational problems arise related to the choice of a particular set of patterns. In this paper we study the computational complexity of several of these pattern problems (showing that they are, in general, computationally hard) and we propose so

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Höller, Daniel, Julia Wichlacz, Pascal Bercher, and Gregor Behnke. "Compiling HTN Plan Verification Problems into HTN Planning Problems." Proceedings of the International Conference on Automated Planning and Scheduling 32 (June 13, 2022): 145–50. http://dx.doi.org/10.1609/icaps.v32i1.19795.

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Plan Verification is the task of deciding whether a sequence of actions is a solution for a given planning problem. In HTN planning, the task is computationally expensive and may be up to NP-hard. However, there are situations where it needs to be solved, e.g. when a solution is post-processed, in systems using approximation, or just to validate whether a planning system works correctly (e.g. for debugging or in a competition). There are verification systems based on translations to propositional logic and on techniques from parsing. Here we present a third approach and translate HTN plan veri

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18

Labaran, Z. I., and N. Y. Adamu. "PHYSICS BASED ALGORITHMS FOR FUZZY TRANSPORTATION PROBLEM." Open Journal of Educational Development (ISSN: 2734-2050) 5, no. 1 (2024): 60–71. https://doi.org/10.52417/ojed.v5i1.729.

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Transportation problems are computationally challenging due to their nondeterministic polynomial-time hard (NP-hard) nature, especially when uncertainties in costs, supply, and demand are involved. The fuzzy transportation problem addresses these uncertainties by representing them as fuzzy numbers, requiring robust methods for ranking and solving optimization models. Traditional exact methods become impractical for such problems, leading to the adoption of metaheuristic approaches. This study introduces three novel physics-based algorithms: one single-point-based and two population-based metho

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Argenziano, Rossella, and Itzhak Gilboa. "Second-order induction in prediction problems." Proceedings of the National Academy of Sciences 116, no. 21 (2019): 10323–28. http://dx.doi.org/10.1073/pnas.1901597116.

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Agents make predictions based on similar past cases, while also learning the relative importance of various attributes in judging similarity. We ask whether the resulting “empirically optimal similarity function” (EOSF) is unique and how easy it is to find it. We show that with many observations and few relevant variables, uniqueness holds. By contrast, when there are many variables relative to observations, nonuniqueness is the rule, and finding the EOSF is computationally hard. The results are interpreted as providing conditions under which rational agents who have access to the same observa

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20

Iori, Manuel, and Silvano Martello. "An annotated bibliography of combined routing and loading problems." Yugoslav Journal of Operations Research 23, no. 3 (2013): 311–26. http://dx.doi.org/10.2298/yjor130315032i.

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Transportation problems involving routing and loading at the same time are currently a hot topic in combinatorial optimization. The interest of researchers and practitioners is motivated by the intrinsic difficulty of this research area, which combines two computationally hard problems, and by its practical relevance in important real world applications. This annotated bibliography aims at collecting, in a systematic way, the most relevant results obtained in the area of vehicle routing with loading constraints, with the objective of stimulating further research in this promising area.

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Stachowiak, Krzysztof, and Piotr Zwierzykowski. "Comparison of Multicast Algorithm Evaluation Results in Low and High Multicast Saturation Environments." Journal of Telecommunications and Information Technology 3 (September 30, 2019): 3–7. http://dx.doi.org/10.26636/jtit.2019.135019.

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The multicast quality of service-enabled routing is a computationally challenging task. Despite ongoing research eﬀorts, the associated mathematical problems are still considered to be NP-hard. In certain applications, computational complexity of ﬁnding the optimal connection between a set of network devices may be a particularly diﬃcult challenge. For example, connecting a small group of participants of a teleconference is not much more complex than setting up a set of mutual point-to-point connections. On the other hand, satisfying the demand for such services as IPTV, with their receivers c

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Porreca, Antonio E., Alberto Leporati, Giancarlo Mauri, and Claudio Zandron. "Elementary Active Membranes Have the Power of Counting." International Journal of Natural Computing Research 2, no. 3 (2011): 35–48. http://dx.doi.org/10.4018/jncr.2011070104.

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P systems with active membranes have the ability of solving computationally hard problems. In this paper, the authors prove that uniform families of P systems with active membranes operating in polynomial time can solve the whole class of PP decision problems, without using nonelementary membrane division or dissolution rules. This result also holds for families having a stricter uniformity condition than the usual one.

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Duc, Nguyen Tan, Nguyen Nam Hai, and Nguyen Hieu Minh. "Blind multi-signature scheme based on factoring and discrete logarithm problem." TELKOMNIKA Telecommunication, Computing, Electronics and Control 17, no. 5 (2019): 2327–34. https://doi.org/10.12928/TELKOMNIKA.v17i5.10525.

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One of the important objectives of information security systems is providing authentication of the electronic documents and messages. In that, blind signature schemes are an important solution to protect the privacy of users in security electronic transactions by highlighting the anonymity of participating parties. Many studies have focused on blind signature schemes, however, most of the studied schemes are based on single computationally difficult problem. Also, digital signature schemes from two difficult problems were proposed but the fact is that only finding solution to single hard probl

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Sheibani, Kaveh. "Fuzzy Greedy Search." International Journal of Applied Management Sciences and Engineering 4, no. 2 (2017): 1–12. http://dx.doi.org/10.4018/ijamse.2017070101.

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This paper presents mathematics of the so-called fuzzy greedy evaluation concept which can be integrated into approaches for hard combinatorial optimization problems. The proposed method evaluates objects in a way that combines fuzzy reasoning with a greedy mechanism, thereby exploiting a fuzzy solution space using greedy methods. Given that the greedy algorithms are computationally inexpensive compared to other more sophisticated methods for combinatorial optimization; this shows practical significance of using the proposed approach. The effectiveness and efficiency of the proposed method are

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25

Park, Dongjoo, Laurence R. Rilett, and Changho Choi. "A class of multicriteria shortest path problems for real-time in-vehicle routing." Canadian Journal of Civil Engineering 34, no. 9 (2007): 1096–109. http://dx.doi.org/10.1139/l07-013.

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In-route guidance systems fastest path routing has typically been adopted because of its simplicity. However, empirical studies on route choice behavior have shown that drivers use numerous criteria in choosing a route. The objective of this paper is to develop computationally efficient algorithms for identifying a manageable subset of the nondominated (i.e., Pareto optimal) paths for real-time in-vehicle routing. The basic notion of the proposed approach is that (i) enumerating all nondominated paths is computationally too expensive, (ii) obtaining a stable mathematical representation of the

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Boyd, Sylvia, and Maryam Haghighi. "A FAST METHOD FOR LARGE-SCALE MULTICHROMOSOMAL BREAKPOINT MEDIAN PROBLEMS." Journal of Bioinformatics and Computational Biology 10, no. 01 (2012): 1240008. http://dx.doi.org/10.1142/s0219720012400082.

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We provide a computationally realistic mathematical framework for the NP-hard problem of the multichromosomal breakpoint median for linear genomes that can be used in constructing phylogenies. A novel approach is provided that can handle signed, unsigned, and partially signed cases of the multichromosomal breakpoint median problem. Our method provides an avenue for incorporating biological assumptions (whenever available) such as the number of chromosomes in the ancestor, and thus it can be tailored to obtain a more biologically relevant picture of the median. We demonstrate the usefulness of

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27

Guckert, Michael. "Simulating Dynamic Vehicle Routing Problems with Athos." Anwendungen und Konzepte der Wirtschaftsinformatik, no. 10 (December 19, 2019): 7. http://dx.doi.org/10.26034/lu.akwi.2019.3248.

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Complex routing problems, such as vehicle routing problems with additional constraints, are both hard to solve and hard to express in a form that is accessible to the human expert and at the same time processible by a computer system that is supposed to produce a solution of suf�?cient quality. The formulation must be formal enough to avoid ambiguities and also comprehensible enough to be created, discussed and shared by domain experts. In this paper, we present the domain speci�?c language Athos in which complex routing problems can be expressed in a computationally independent, human-readabl

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Prajapati, Raju, Om Prakash Dubey, and Randhir Kumar. "IMPROVED PARTICLE SWARM OPTIMIZATION FOR NON-LINEAR PROGRAMMING PROBLEM WITH BARRIER METHOD." International Journal of Students' Research in Technology & Management 5, no. 4 (2017): 72–80. http://dx.doi.org/10.18510/ijsrtm.2017.5410.

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The Non-Linear Programming Problems (NLPP) are computationally hard to solve as compared to the Linear Programming Problems (LPP). To solve NLPP, the available methods are Lagrangian Multipliers, Sub gradient method, Karush-Kuhn-Tucker conditions, Penalty and Barrier method etc. In this paper, we are applying Barrier method to convert the NLPP with equality constraint to an NLPP without constraint. We use the improved version of famous Particle Swarm Optimization (PSO) method to obtain the solution of NLPP without constraint. SCILAB programming language is used to evaluate the solution on samp

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Liu, Zhenqiu, and Gang Li. "Efficient Regularized Regression withL0Penalty for Variable Selection and Network Construction." Computational and Mathematical Methods in Medicine 2016 (2016): 1–11. http://dx.doi.org/10.1155/2016/3456153.

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Variable selections for regression with high-dimensional big data have found many applications in bioinformatics and computational biology. One appealing approach is theL0regularized regression which penalizes the number of nonzero features in the model directly. However, it is well known thatL0optimization is NP-hard and computationally challenging. In this paper, we propose efficient EM (L0EM) and dualL0EM (DL0EM) algorithms that directly approximate theL0optimization problem. WhileL0EM is efficient with large sample size, DL0EM is efficient with high-dimensional (n≪m) data. They also provid

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Singh, Amrit Pal, Chetna Gupta, Rashpal Singh, and Nandini Singh. "A Comparative Analysis of Evolutionary Algorithms for Data Classification Using KEEL Tool." International Journal of Swarm Intelligence Research 12, no. 1 (2021): 17–28. http://dx.doi.org/10.4018/ijsir.2021010102.

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Evolutionary algorithms are inspired by the biological model of evolution and natural selection and are used to solve computationally hard problems, also known as NP-hard problems. The main motive to use these algorithms is their robust and adaptive nature to provide best search techniques for complex problems. This paper presents a comparative analysis of classification of algorithm's family instead of algorithms comparison using KEEL tool. This work compares SSMA-C, DROP3PSO-C, FURIA-C, GFS-MaxLogitBoost-Cand CPSO-C algorithms. Further, these selected evolutionary algorithms are compared aga

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Moghadam, Ali Mokhtari, Hamed Piroozfard, Azanizawati Bt Ma'aram, and Seyed Ali Mirzapour. "Solving a Capacitated p-Median Location Allocation Problem Using Genetic Algorithm: A Case Study." Advanced Materials Research 845 (December 2013): 569–73. http://dx.doi.org/10.4028/www.scientific.net/amr.845.569.

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Facility location-allocation problems have various applications in private and public sectors. A capacitated p-median problem is considered in this work which is computationally NP-Hard. The primary goal of this paper was to determine a set of p-facilities location in which all demand points are allocated and its average distance traveled from the customers’ location to the selected p-facilities is minimized. In addition, the model also considered supplier’s allocation for p facilities. A real world case study has been addressed, and genetic algorithm which consists of crossover and mutation o

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Liu, Shengcai, Ke Tang, and Xin Yao. "Automatic Construction of Parallel Portfolios via Explicit Instance Grouping." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 1560–67. http://dx.doi.org/10.1609/aaai.v33i01.33011560.

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Exploiting parallelism is becoming more and more important in designing efficient solvers for computationally hard problems. However, manually building parallel solvers typically requires considerable domain knowledge and plenty of human effort. As an alternative, automatic construction of parallel portfolios (ACPP) aims at automatically building effective parallel portfolios based on a given problem instance set and a given rich configuration space. One promising way to solve the ACPP problem is to explicitly group the instances into different subsets and promote a component solver to handle

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Păun, Gheorghe, Mario J. Perez-Jimenez, and Agustín Riscos-Nunez. "Tissue P Systems with Cell Division." International Journal of Computers Communications & Control 3, no. 3 (2008): 295. http://dx.doi.org/10.15837/ijccc.2008.3.2397.

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In tissue P systems several cells (elementary membranes) communicate through symport/antiport rules, thus carrying out a computation. We add to such systems the basic feature of (cell–like) P systems with active membranes – the possibility to divide cells. As expected (as it is the case for P systems with active membranes), in this way we get the possibility to solve computationally hard problems in polynomial time; we illustrate this possibility with SAT problem.

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Rudner-Halász, Tamás, Wolfgang Porod, and Gyorgy Csaba. "Oscillator-Based Processing Unit for Formant Recognition." Information 16, no. 7 (2025): 611. https://doi.org/10.3390/info16070611.

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Oscillatory neural networks have so far been successfully applied to a number of computing problems, such as associative memories, or to handle computationally hard tasks. In this paper, we show how to use oscillators to process time-dependent waveforms with minimal or no preprocessing. Since preprocessing and first-layer processing are often the most power-hungry steps in neural networks, our findings may open new doors to simple and power-efficient edge-AI devices.

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Pérez-Jiménez, Mario J. "The P versus NP Problem from the Membrane Computing View." European Review 22, no. 1 (2014): 18–33. http://dx.doi.org/10.1017/s1062798713000598.

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In the last few decades several computing models using powerful tools from Nature have been developed (because of this, they are known as bio-inspired models). Commonly, the space-time trade-off method is used to develop efficient solutions to computationally hard problems. According to this, implementation of such models (in biological, electronic, or any other substrate) would provide a significant advance in the practical resolution of hard problems. Membrane Computing is a young branch of Natural Computing initiated by Gh. Păun at the end of 1998. It is inspired by the structure and functi

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NORDIN, THOMAS, and ANDREW TOLMACH. "Modular lazy search for Constraint Satisfaction Problems." Journal of Functional Programming 11, no. 5 (2001): 557–87. http://dx.doi.org/10.1017/s0956796801004051.

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We describe a unified, lazy, declarative framework for solving constraint satisfaction problems, an important subclass of combinatorial search problems. These problems are both practically significant and computationally hard. Finding solutions involves combining good general-purpose search algorithms with problem-specific heuristics. Conventional imperative algorithms are usually implemented and presented monolithically, which makes them hard to understand and reuse, even though new algorithms often are combinations of simpler ones. Lazy functional languages, such as Haskell, encourage modula

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Ciliberto, Carlo, Mark Herbster, Alessandro Davide Ialongo, et al. "Quantum machine learning: a classical perspective." Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474, no. 2209 (2018): 20170551. http://dx.doi.org/10.1098/rspa.2017.0551.

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Recently, increased computational power and data availability, as well as algorithmic advances, have led machine learning (ML) techniques to impressive results in regression, classification, data generation and reinforcement learning tasks. Despite these successes, the proximity to the physical limits of chip fabrication alongside the increasing size of datasets is motivating a growing number of researchers to explore the possibility of harnessing the power of quantum computation to speed up classical ML algorithms. Here we review the literature in quantum ML and discuss perspectives for a mix

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Lualdi, Pietro, Ralf Sturm, and Tjark Siefkes. "A Multi-Fidelity Successive Response Surface Method for Crashworthiness Optimization Problems." Applied Sciences 13, no. 20 (2023): 11452. http://dx.doi.org/10.3390/app132011452.

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Due to the high computational burden and the high non-linearity of the responses, crashworthiness optimizations are notoriously hard-to-solve challenges. Among various approaches, methods like the Successive Response Surface Method (SRSM) have stood out for their efficiency in enhancing baseline designs within a few iterations. However, these methods have limitations that restrict their application. Their minimum iterative resampling required is often computationally prohibitive. Furthermore, surrogate models are conventionally constructed using Polynomial Response Surface (PRS), a method that

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Pakin, Scott, and Steven P. Reinhardt. "Programming a D-wave annealing-based quantum computer: tools and techniques." quantum Information and Computation 19, no. 9&10 (2019): 721–59. http://dx.doi.org/10.26421/qic19.9-10-1.

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Quantum annealing is a form of quantum computing that exploits quantum effects to probabilistically solve a specific, NP-hard problem: finding the ground state of a classical, Ising-model Hamiltonian. Because physical quantum annealers are already available, there exists the pressing question of how to program such systems. That is, how can one map a computational problem into the coefficients of an Ising-model Hamiltonian for solution by quantum-annealing hardware? In this article, we address that question primarily from a practical standpoint. We survey extant software tools intended for pro

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Parihar, Abhinav, Nikhil Shukla, Matthew Jerry, Suman Datta, and Arijit Raychowdhury. "Computing with dynamical systems based on insulator-metal-transition oscillators." Nanophotonics 6, no. 3 (2017): 601–11. http://dx.doi.org/10.1515/nanoph-2016-0144.

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AbstractIn this paper, we review recent work on novel computing paradigms using coupled oscillatory dynamical systems. We explore systems of relaxation oscillators based on linear state transitioning devices, which switch between two discrete states with hysteresis. By harnessing the dynamics of complex, connected systems, we embrace the philosophy of “let physics do the computing” and demonstrate how complex phase and frequency dynamics of such systems can be controlled, programmed, and observed to solve computationally hard problems. Although our discussion in this paper is limited to insula

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Usmanov, S. R., G. V. Salakhov, A. A. Bozhedarov, E. O. Kiktenko, and A. K. Fedorov. "Quantum and quantum-inspired optimization for an in-core fuel management problem." Journal of Physics: Conference Series 2701, no. 1 (2024): 012031. http://dx.doi.org/10.1088/1742-6596/2701/1/012031.

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Abstract Operation management of nuclear power plants consists of several computationally hard problems. Searching for an in-core fuel loading pattern is among them. The main challenge of this combinatorial optimization problem is the exponential growth of the search space with a number of loading elements. Here we study a reloading problem in a Quadratic Unconstrained Binary Optimization (QUBO) form. Such a form allows us to apply various techniques, including quantum annealing, classical simulated annealing, and quantum-inspired algorithm in order to find fuel reloading patterns for several

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Sharma, Ritvik, and Sara Achour. "Compilation of Qubit Circuits to Optimized Qutrit Circuits." Proceedings of the ACM on Programming Languages 8, PLDI (2024): 272–95. http://dx.doi.org/10.1145/3656388.

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Quantum computers are a revolutionary class of computational platforms that are capable of solving computationally hard problems. However, today’s quantum hardware is subject to noise and decoherence issues that together limit the scale and complexity of the quantum circuits that can be implemented. Recently, practitioners have developed qutrit-based quantum hardware platforms that compute over 0, 1, and 2 states, and have presented circuit depth reduction techniques using qutrits’ higher energy 2 states to temporarily store information. However, thus far, such quantum circuits that use higher

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Galetzka, Armin, Dimitrios Loukrezis, and Herbert De Gersem. "Three-dimensional data-driven magnetostatic field computation using real-world measurement data." COMPEL - The international journal for computation and mathematics in electrical and electronic engineering 41, no. 2 (2021): 615–27. http://dx.doi.org/10.1108/compel-06-2021-0219.

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Purpose The purpose of this paper is to present the applicability of data-driven solvers to computationally demanding three-dimensional problems and their practical usability when using real-world measurement data. Design/methodology/approach Instead of using a hard-coded phenomenological material model within the solver, the data-driven computing approach reformulates the boundary value problem such that the field solution is directly computed on raw measurement data. The data-driven formulation results in a double minimization problem based on Lagrange multipliers, where the sought solution

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McCreesh, Ciaran, Patrick Prosser, Christine Solnon, and James Trimble. "When Subgraph Isomorphism is Really Hard, and Why This Matters for Graph Databases." Journal of Artificial Intelligence Research 61 (March 30, 2018): 723–59. http://dx.doi.org/10.1613/jair.5768.

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The subgraph isomorphism problem involves deciding whether a copy of a pattern graph occurs inside a larger target graph. The non-induced version allows extra edges in the target, whilst the induced version does not. Although both variants are NP-complete, algorithms inspired by constraint programming can operate comfortably on many real-world problem instances with thousands of vertices. However, they cannot handle arbitrary instances of this size. We show how to generate "really hard" random instances for subgraph isomorphism problems, which are computationally challenging with a couple of h

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Olvera, María Dolores Gómez, Juan Antonio López Ramos, and Blas Torrecillas Jover. "Public Key Protocols over Twisted Dihedral Group Rings." Symmetry 11, no. 8 (2019): 1019. http://dx.doi.org/10.3390/sym11081019.

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Key management is a central problem in information security. The development of quantum computation could make the protocols we currently use unsecure. Because of that, new structures and hard problems are being proposed. In this work, we give a proposal for a key exchange in the context of NIST recommendations. Our protocol has a twisted group ring as setting, jointly with the so-called decomposition problem, and we provide a security and complexity analysis of the protocol. A computationally equivalent cryptosystem is also proposed.

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Paul, Kamakhya, Pinkimani Goswami, and Madan Mohan Singh. "ALGEBRAIC BRAID GROUP PUBLIC KEY CRYPTOGRAPHY." jnanabha 52, no. 02 (2022): 218–23. http://dx.doi.org/10.58250/jnanabha.2022.52225.

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The braid group cryptography arises with the involvement of the braid group, which is an infinite non-commutative group arising from geometric braids. In this paper, we have proposed a new public key cryptosystem based on braid group. The security of our proposed scheme is based on two hard problems on braid group, conjugacy search problem and p-th root problem on braid group. We also checked the one-wayness, semantic security and efficiency of our proposed scheme, and found it to be computationally secured

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An, Jin Liang, Jia Gao, Jin Hui Lei, and Guo Hong Gao. "An Improved Algorithm for TSP Problem Solving with Hopfield Neural Networks." Advanced Materials Research 143-144 (October 2010): 538–42. http://dx.doi.org/10.4028/www.scientific.net/amr.143-144.538.

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Hopfield and Tank have shown that neural networks can be used to solve certain computationally hard problems, in particular they studied the Traveling Salesman Problem (TSP). In this paper,on the base of the analysis of tradiontial methord,introduced an improved algorithm for TSP Problem Solving with Hopfield Neural Networks.We found the accuracy of the results depend on the initial parameters to a large extent, discussed how to set initial parameters properly; analysed the internal relationship between the terms in energy function, and improved the energy function. Used a fixed starting point

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Dr. Nadia Ahmed. "Quantum Computing Algorithms for Integer Factorization: A Comparative Analysis." Modern Dynamics: Mathematical Progressions 1, no. 1 (2024): 6–9. http://dx.doi.org/10.36676/mdmp.v1.i1.02.

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The field of quantum computing has witnessed remarkable advancements in recent years, particularly in its potential applications to solve computationally hard problems such as integer factorization. a comparative analysis of quantum computing algorithms for integer factorization, focusing on their theoretical foundations, computational complexity, and practical implications. We review prominent algorithms such as Shor's algorithm, which leverages quantum parallelism and period finding to efficiently factor large composite integers, and compare them with classical factorization algorithms like

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JIANG, TAO, EDWARD McDOWELL, and B. RAVIKUMAR. "THE STRUCTURE AND COMPLEXITY OF MINIMAL NFA’S OVER A UNARY ALPHABET." International Journal of Foundations of Computer Science 02, no. 02 (1991): 163–82. http://dx.doi.org/10.1142/s012905419100011x.

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Many difficult open problems in theoretical computer science center around non-determinism. We study the fundamental problem of converting a given deterministic finite automaton (DFA) to a minimal nondeterministic finite automaton (NFA). Despite extensive work on finite automata, this fundamental problem has remained open. Recently, we studied this problem and showed that this (and related) problems are computationally hard.11 Herewe study the restriction of this problem to the case when the input DFA is over a one-letter alphabet. Even in this restricted case the problem is computationally ha

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Thilmany, Jean. "Ask the Supercomputer." Mechanical Engineering 128, no. 04 (2006): 36–38. http://dx.doi.org/10.1115/1.2006-apr-3.

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This paper analyzes research work on developing techniques to study complex fluids. Although several computational fluid dynamics (CFD) vendors now sell desktop software that mechanical engineers can buy to model complex flows, many problems are still simply too hard for those applications. According to engineers, CFD programs for these complex problems can take years to write, even with the supercomputer's aid. Moreover, some flows may never be modeled: they are just too complex for even the most advanced software. Behr and a colleague, Matteo Pasquali, an Associate Professor in the Departmen

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