Academic literature on the topic 'Unconstrained binary quadratic'
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Unconstrained binary quadratic.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Journal articles on the topic "Unconstrained binary quadratic"
Verma, Amit, and Mark Lewis. "Goal seeking Quadratic Unconstrained Binary Optimization." Results in Control and Optimization 7 (June 2022): 100125. http://dx.doi.org/10.1016/j.rico.2022.100125.
Full textLewis, Mark, John Metcalfe, and Gary Kochenberger. "Robust optimisation of unconstrained binary quadratic problems." International Journal of Operational Research 36, no. 4 (2019): 441. http://dx.doi.org/10.1504/ijor.2019.10025701.
Full textLewis, Mark, John Metcalfe, and Gary Kochenberger. "Robust optimisation of unconstrained binary quadratic problems." International Journal of Operational Research 36, no. 4 (2019): 441. http://dx.doi.org/10.1504/ijor.2019.104050.
Full textBoettcher, S. "Extremal Optimization for Quadratic Unconstrained Binary Problems." Physics Procedia 68 (2015): 16–19. http://dx.doi.org/10.1016/j.phpro.2015.07.102.
Full textWang, Yang, Zhipeng Lü, Fred Glover, and Jin-Kao Hao. "Path relinking for unconstrained binary quadratic programming." European Journal of Operational Research 223, no. 3 (December 2012): 595–604. http://dx.doi.org/10.1016/j.ejor.2012.07.012.
Full textKochenberger, Gary, Jin-Kao Hao, Fred Glover, Mark Lewis, Zhipeng Lü, Haibo Wang, and Yang Wang. "The unconstrained binary quadratic programming problem: a survey." Journal of Combinatorial Optimization 28, no. 1 (April 18, 2014): 58–81. http://dx.doi.org/10.1007/s10878-014-9734-0.
Full textGlover, Fred, and Jin-Kao Hao. "f-Flip strategies for unconstrained binary quadratic programming." Annals of Operations Research 238, no. 1-2 (December 11, 2015): 651–57. http://dx.doi.org/10.1007/s10479-015-2076-1.
Full textRahmeh, Samer, and Adam Neumann. "HUBO & QUBO and Prime Factorization." International Journal of Bioinformatics and Intelligent Computing 3, no. 1 (February 20, 2024): 45–69. http://dx.doi.org/10.61797/ijbic.v3i1.301.
Full textMerz, Peter, and Kengo Katayama. "Memetic algorithms for the unconstrained binary quadratic programming problem." Biosystems 78, no. 1-3 (December 2004): 99–118. http://dx.doi.org/10.1016/j.biosystems.2004.08.002.
Full textLiefooghe, Arnaud, Sébastien Verel, and Jin-Kao Hao. "A hybrid metaheuristic for multiobjective unconstrained binary quadratic programming." Applied Soft Computing 16 (March 2014): 10–19. http://dx.doi.org/10.1016/j.asoc.2013.11.008.
Full textDissertations / Theses on the topic "Unconstrained binary quadratic"
Battikh, Rabih. "La résοlutiοn de prοblème quadratique binaire par des méthοdes d'οptimisatiοn exactes et apprοchées." Electronic Thesis or Diss., Normandie, 2024. http://www.theses.fr/2024NORMLH20.
Full textIn this thesis, we presented a new hybrid algorithm (HA) for solving the unconstrained quadratic programming problem (UQP). This algorithm is based on the combination of a block of five special procedures and the simulated annealing method. Our procedures are very efficient and fast, but unfortunately, they sometimes get stuck in a local minimum. To overcome this drawback, we combined them with a simulated annealing algorithm. Then, we repeated these procedures several times to obtain the best solution using our hybrid algorithm.We noticed that the gap between the solution found by (HA) and the CPLEX software is very small, which implies the efficiency of our strategy. Moreover, we integrated our hybrid method into a semi-definite relaxation problem of (UQP) within a branch and bound strategy. To facilitate the resolution of (UQP), we suggest applying fixing criteria to reduce the size of the problem and speed up the process of obtaining an exact solution. The quality of the lower bound found by our code (QPTOSDP) is very good, but the execution time increases with the size of the problem. Numerical results prove the accuracy of our optimal solution and the efficiency and robustness of our approach.We extended the fixing criteria to the quadratic programming problem (QP), which in some cases allows reducing the dimension of the problem, or even solving it entirely by applying a repetition loop based on these criteria
CIRILLO, GIOVANNI AMEDEO. "Engineering quantum computing technologies: from compact modelling to applications." Doctoral thesis, Politecnico di Torino, 2022. http://hdl.handle.net/11583/2971119.
Full textTavares, Gabriel. "New algorithms for Quadratic Unconstrained Binary Optimization (QUBO) with applications in engineering and social sciences." 2008. http://hdl.rutgers.edu/1782.2/rucore10001600001.ETD.000051095.
Full textGomes, Cláudio Filipe Prata. "Portfolio Optimization in Financial Markets using Quantum Computing: An Experimental Study." Master's thesis, 2021. http://hdl.handle.net/10316/96124.
Full textA computação quântica está prestes a mudar o mundo tal como o conhecemos. Através da exploração das propriedades da teoria quântica para fins computacionais, é esperada uma redução substancial na quantidade de problemas que hoje são considerados intratáveis. Isto significa que os computadores quânticos têm a capacidade de devolver soluções para alguns problemas de interesse prático para os quais um computador clássico não consegue devolver, pelo menos em tempo útil. Isto é ainda mais revolucionário e notável pelo facto de que esses problemas abrangem domínios multidisciplinares como Química, Medicina e, mais relevante no contexto desta dissertação, Finanças.Neste trabalho, vamos focar-nos na utilização da computação quântica para abordar um problema relevante e atual no domínio financeiro. Mais especificamente, um problema de otimização combinatorial, o problema de otimização de portfólios, que consiste em selecionar o melhor portfólio financeiro (combinação de ativos) entre um conjunto de todos os portfólios possíveis, de acordo com uma certa função objetivo, comummente de forma a maximizar o retorno esperado ou minimizar o risco. Devido ao grande número de parâmetros, como o retorno esperado por ativo e as condições de mercado, este problema atinge uma complexidade exponencial e é um problema NP-hard, intratável no contexto da computação clássica.Nós desenvolvemos um estudo empírico acerca da influência dos parâmetros nas soluções devolvidas por um computador quântico para o problema de otimização de portfólios. Em particular, utilizamos um computador quântico da D-Wave e variamos os parâmetros relacionados não só com o computador quântico, mas também com o problema de otimização de portfólios. Acreditamos que as conclusões do estudo são contribuições úteis para qualquer investigador que deseje utilizar computadores quânticos adiabáticos no contexto do problema de otimização de portfólios e também noutros domínios de aplicação.As nossas descobertas sugerem que os parâmetros têm efeito nos resultados, quer sejam relacionados com o problema de otimização de portfólios ou com o computador quântico. Além disso, também descobrimos que alguns dos parâmetros têm um grande impacto, tal como o chain strength, que define a força com a qual os qubits que representam uma variável estão correlacionados, e que outros não têm nenhum efeito estatisticamente significativo, tais como o anneal schedule ou o embedding.
Quantum computing is bound to change the world as we know it. By exploring the properties of quantum theory for computational purposes, it is expected to substantially reduce the amount of problems that are nowadays considered computationally intractable. This means that quantum computers have the power of providing solutions for some of the problems of practical interest for which a classical computer cannot, at least in a timely manner. This is even more revolutionary and remarkable given the fact these problems range from multidisciplinary domains such as Chemistry, Medicine, and, most relevant in the context of this dissertation, Finance.In this work, we will focus on leveraging quantum computing to addressing a relevant and timely problem within the financial domain. We will target a combinatorial optimization problem, the portfolio optimization problem, which consists of selecting the best portfolio (combination of assets) among all possible portfolios, according to some objective function, whether to maximize return or minimize risk. Due to the high number of parameters, such as the expected return per asset and market conditions, this problem attains an exponential complexity and is an NP-hard problem, intractable in the context of classical computing.We designed and conducted an empirical study on the effect of parameters on solutions to the portfolio optimization problem given by a quantum computer. In particular, we use a quantum computer from D-Wave and vary the parameters related to not only the quantum computer, but also to the portfolio optimization problem itself. We believe that our findings are useful not only for those using adiabatic quantum computers in the context of portfolio optimization problem, and also in other application domains.Our findings suggest that the parameters do have an effect on the results, whether they are related to the portfolio optimization problem or to the quantum computer. Moreover, we found that some of the parameters have a great impact, such as the chain strength, which defines the strength associated to the couplings between qubits that represent a variable, and that other parameters have no statistically significant effect, such as the anneal schedule or embedding used.
Silva, Pedro Miguel Dias da. "Quantum Computing for Optimizing Power Flow in Energy Grids." Master's thesis, 2021. http://hdl.handle.net/10316/98073.
Full textQuantum Computing is beginning to gather even more attention at a time where efforts are being made into familiarizing younger audiences into not only learning programming on a classical computer, but also on a quantum one.This new paradigm of computation is set to revolutionize several industries as the hardware keeps developing, with the potential to solve problems that a classical computer would consider intangible, as well as giving some specific problems a so sought after speed-up. This is done by applying the properties of quantum physics, like superposition and entanglement, for computation. These properties not only allow to process a larger amount of data simultaneously, but also allows to tackle problems in a completely different way that would not be possible in a classical computer.This thesis focuses on solving a known and relevant problem in the electrical industry and studying its application on a quantum environment. The Unit Commitment Problem, the problem in question, consists in minimizing the cost of power production, for a certain time horizon, by scheduling different generating units in order to meet a certain demand given by a valid forecast. Given that this is an NP-hard problem, it quickly becomes intractable on classical computers when considering real world scenarios on a large scale.A test scenario was also designed to study, by conducting an experimental analysis, the influences that each of the parameters have on the solution quality. To that end, the formulation of the Unit Commitment Problem was also translated to a suitable QUBO form which is then solved through a quantum annealer from D-Wave. For that test scenario, both the parameters from the problem formulation as well as the parameters related to the quantum computer were considered.The results from the experimental analysis suggest that most parameters do have an impact on the solution quality. With some having a greater impact overall such as Grids, that are representing how accurate the linearization of the problem is, as well the delta value associated with the first constraint, a value that is tied to how much of a weight the first constraint, that restricts each unit to a single production level, has. While the parameters with the overall greater impact are tied to the formulation of the problem, parameters like chain strength that affects the strength of coupling between qubits representing a single variable also have a significant impact on the solution quality. While most parameters have a statistical impact on the solution quality, the delta associated with the second constraint, that restricts power generation to equal the demand, fails to have an impact.
Quantum Computing is beginning to gather even more attention at a time where efforts are being made into familiarizing younger audiences into not only learning programming on a classical computer, but also on a quantum one.This new paradigm of computation is set to revolutionize several industries as the hardware keeps developing, with the potential to solve problems that a classical computer would consider intangible, as well as giving some specific problems a so sought after speed-up. This is done by applying the properties of quantum physics, like superposition and entanglement, for computation. These properties not only allow to process a larger amount of data simultaneously, but also allows to tackle problems in a completely different way that would not be possible in a classical computer.This thesis focuses on solving a known and relevant problem in the electrical industry and studying its application on a quantum environment. The Unit Commitment Problem, the problem in question, consists in minimizing the cost of power production, for a certain time horizon, by scheduling different generating units in order to meet a certain demand given by a valid forecast. Given that this is an NP-hard problem, it quickly becomes intractable on classical computers when considering real world scenarios on a large scale.A test scenario was also designed to study, by conducting an experimental analysis, the influences that each of the parameters have on the solution quality. To that end, the formulation of the Unit Commitment Problem was also translated to a suitable QUBO form which is then solved through a quantum annealer from D-Wave. For that test scenario, both the parameters from the problem formulation as well as the parameters related to the quantum computer were considered.The results from the experimental analysis suggest that most parameters do have an impact on the solution quality. With some having a greater impact overall such as Grids, that are representing how accurate the linearization of the problem is, as well the delta value associated with the first constraint, a value that is tied to how much of a weight the first constraint, that restricts each unit to a single production level, has. While the parameters with the overall greater impact are tied to the formulation of the problem, parameters like chain strength that affects the strength of coupling between qubits representing a single variable also have a significant impact on the solution quality. While most parameters have a statistical impact on the solution quality, the delta associated with the second constraint, that restricts power generation to equal the demand, fails to have an impact.
Books on the topic "Unconstrained binary quadratic"
Punnen, Abraham P., ed. The Quadratic Unconstrained Binary Optimization Problem. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-04520-2.
Full textQuadratic Unconstrained Binary Optimization Problem: Theory, Algorithms, and Applications. Springer International Publishing AG, 2023.
Find full textQuadratic Unconstrained Binary Optimization Problem: Theory, Algorithms, and Applications. Springer International Publishing AG, 2022.
Find full textBook chapters on the topic "Unconstrained binary quadratic"
Kochenberger, Gary A., Fred Glover, and Haibo Wang. "Binary Unconstrained Quadratic Optimization Problem." In Handbook of Combinatorial Optimization, 533–57. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-7997-1_15.
Full textPunnen, Abraham P. "Introduction to QUBO." In The Quadratic Unconstrained Binary Optimization Problem, 1–37. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-04520-2_1.
Full textÇela, Eranda, and Abraham P. Punnen. "Complexity and Polynomially Solvable Special Cases of QUBO." In The Quadratic Unconstrained Binary Optimization Problem, 57–95. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-04520-2_3.
Full textPunnen, Abraham P., and Renata Sotirov. "Mathematical Programming Models and Exact Algorithms." In The Quadratic Unconstrained Binary Optimization Problem, 139–85. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-04520-2_6.
Full textLetchford, Adam N. "The Boolean Quadric Polytope." In The Quadratic Unconstrained Binary Optimization Problem, 97–120. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-04520-2_4.
Full textWang, Yang, and Jin-Kao Hao. "Metaheuristic Algorithms." In The Quadratic Unconstrained Binary Optimization Problem, 241–59. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-04520-2_9.
Full textBoros, Endre. "Autarkies and Persistencies for QUBO." In The Quadratic Unconstrained Binary Optimization Problem, 121–37. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-04520-2_5.
Full textWoods, Brad D., Gary Kochenberger, and Abraham P. Punnen. "QUBO Software." In The Quadratic Unconstrained Binary Optimization Problem, 301–11. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-04520-2_11.
Full textNatarajan, Karthik. "The Random QUBO." In The Quadratic Unconstrained Binary Optimization Problem, 187–206. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-04520-2_7.
Full textPunnen, Abraham P. "The Bipartite QUBO." In The Quadratic Unconstrained Binary Optimization Problem, 261–300. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-04520-2_10.
Full textConference papers on the topic "Unconstrained binary quadratic"
Iftakher, Ashfaq, and M. M. Faruque Hasan. "Exploring Quantum Optimization for Computer-aided Molecular and Process Design." In Foundations of Computer-Aided Process Design, 292–99. Hamilton, Canada: PSE Press, 2024. http://dx.doi.org/10.69997/sct.143809.
Full textDe Souza, Murilo Zangari, and Aurora Trinidad Ramirez Pozo. "Multiobjective Binary ACO for Unconstrained Binary Quadratic Programming." In 2015 Brazilian Conference on Intelligent Systems (BRACIS). IEEE, 2015. http://dx.doi.org/10.1109/bracis.2015.15.
Full textBaioletti, Marco. "Probabilistic reasoning as quadratic unconstrained binary optimization." In GECCO '22: Genetic and Evolutionary Computation Conference. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3520304.3534005.
Full textLin, Geng. "Solving unconstrained binary quadratic programming using binary particle swarm optimization." In 2013 International Conference of Information Technology and Industrial Engineering. Southampton, UK: WIT Press, 2013. http://dx.doi.org/10.2495/itie130311.
Full textLiu Liu, Qiuling Xie, and Chunli Liu. "Sufficient optimal conditions for unconstrained quadratic binary problems." In 12th International Symposium on Operations Research and its Applications in Engineering, Technology and Management (ISORA 2015). Institution of Engineering and Technology, 2015. http://dx.doi.org/10.1049/cp.2015.0623.
Full textAlom, Md Zahangir, Brian Van Essen, Adam T. Moody, David Peter Widemann, and Tarek M. Taha. "Quadratic Unconstrained Binary Optimization (QUBO) on neuromorphic computing system." In 2017 International Joint Conference on Neural Networks (IJCNN). IEEE, 2017. http://dx.doi.org/10.1109/ijcnn.2017.7966350.
Full textBorgulya, Istvan. "A parallel evolutionary algorithm for unconstrained binary quadratic problems." In the 10th annual conference. New York, New York, USA: ACM Press, 2008. http://dx.doi.org/10.1145/1389095.1389213.
Full textFang, Yan, and Ashwin Sanjay Lele. "Solving Quadratic Unconstrained Binary Optimization with Collaborative Spiking Neural Networks." In 2022 IEEE International Conference on Rebooting Computing (ICRC). IEEE, 2022. http://dx.doi.org/10.1109/icrc57508.2022.00021.
Full textGabor, Thomas, Marian Lingsch Rosenfeld, Claudia Linnhoff-Popien, and Sebastian Feld. "How to Approximate any Objective Function via Quadratic Unconstrained Binary Optimization." In 2022 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER). IEEE, 2022. http://dx.doi.org/10.1109/saner53432.2022.00149.
Full textPauckert, Justin, Matthieu Parizy, and Mayowa Ayodele. "Strategic Solution Combination in Scatter Search for Quadratic Unconstrained Binary Optimization." In 14th International Conference on Evolutionary Computation Theory and Applications. SCITEPRESS - Science and Technology Publications, 2022. http://dx.doi.org/10.5220/0011547600003332.
Full text