Academic literature on the topic 'Algorithm'
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Journal articles on the topic "Algorithm"
Gangavane, Ms H. N. "A Comparison of ABK-Means Algorithm with Traditional Algorithms." International Journal of Trend in Scientific Research and Development Volume-1, Issue-4 (June 30, 2017): 614–21. http://dx.doi.org/10.31142/ijtsrd2197.
Full textToleushova, A. T., D. M. Uypalakova, and A. B. Imansakipova. "SIGNATURE RECOGNITION ALGORITHMS. BEZIER ALGORITHM." Bulletin of Shakarim University. Technical Sciences, no. 3(7) (February 10, 2023): 47–53. http://dx.doi.org/10.53360/2788-7995-2022-1(5)-7.
Full textShaw, Dr Shaik Mohiddin, Dr Dharmaiah Gurram, Hari Krishna Gurram, and Ramakrishna Gurram. "Transitive Closure Algorithm using Binary OR Operation: Primes Algorithm, GHK Algorithm." SIJ Transactions on Computer Science Engineering & its Applications (CSEA) 03, no. 02 (April 23, 2015): 01–05. http://dx.doi.org/10.9756/sijcsea/v3i2/03030100101.
Full textLian, Jian, Yan Zhang, and Cheng Jiang Li. "An Efficient K-Shortest Paths Based Routing Algorithm." Advanced Materials Research 532-533 (June 2012): 1775–79. http://dx.doi.org/10.4028/www.scientific.net/amr.532-533.1775.
Full textHuang, Yuan Jiang, and Jie Huang. "A New Feature Detection Algorithm Based on RANSAC." Advanced Materials Research 971-973 (June 2014): 1477–80. http://dx.doi.org/10.4028/www.scientific.net/amr.971-973.1477.
Full textDeghbouch, Hicham, and Fatima Debbat. "Hybrid Bees Algorithm with Grasshopper Optimization Algorithm for Optimal Deployment of Wireless Sensor Networks." Inteligencia Artificial 24, no. 67 (February 20, 2021): 18–35. http://dx.doi.org/10.4114/intartif.vol24iss67pp18-35.
Full textOkazaki, Hiroyuki, Yosiki Aoki, and Yasunari Shidama. "Extended Euclidean Algorithm and CRT Algorithm." Formalized Mathematics 20, no. 2 (December 1, 2012): 175–79. http://dx.doi.org/10.2478/v10037-012-0020-2.
Full textSami N. Hussein and Nazar K. Hussein. "Improving Moth-Flame Optimization Algorithm by using Slime-Mould Algorithm." Tikrit Journal of Pure Science 27, no. 1 (December 2, 2022): 99–109. http://dx.doi.org/10.25130/tjps.v27i1.86.
Full textBeth, T., and D. Gollman. "Algorithm engineering for public key algorithms." IEEE Journal on Selected Areas in Communications 7, no. 4 (May 1989): 458–66. http://dx.doi.org/10.1109/49.17708.
Full textKulkarni, Anuj, Saish Padave, Satyam Shrivastava, and Mrs Vidya Kawtikwar. "Algorithm Visualizer." International Journal for Research in Applied Science and Engineering Technology 11, no. 7 (July 31, 2023): 1818–23. http://dx.doi.org/10.22214/ijraset.2023.54837.
Full textDissertations / Theses on the topic "Algorithm"
Yarmolskyy, Oleksandr. "Využití distribuovaných a stochastických algoritmů v síti." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2018. http://www.nusl.cz/ntk/nusl-370918.
Full textHarris, Steven C. "A genetic algorithm for robust simulation optimization." Ohio : Ohio University, 1996. http://www.ohiolink.edu/etd/view.cgi?ohiou1178645751.
Full textNyman, Peter. "Representation of Quantum Algorithms with Symbolic Language and Simulation on Classical Computer." Licentiate thesis, Växjö University, School of Mathematics and Systems Engineering, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:vxu:diva-2329.
Full textUtvecklandet av kvantdatorn är ett ytterst lovande projekt som kombinerar teoretisk och experimental kvantfysik, matematik, teori om kvantinformation och datalogi. Under första steget i utvecklandet av kvantdatorn låg huvudintresset på att skapa några algoritmer med framtida tillämpningar, klargöra grundläggande frågor och utveckla en experimentell teknologi för en leksakskvantdator som verkar på några kvantbitar. Då dominerade förväntningarna om snabba framsteg bland kvantforskare. Men det verkar som om dessa stora förväntningar inte har besannats helt. Många grundläggande och tekniska problem som dekoherens hos kvantbitarna och instabilitet i kvantstrukturen skapar redan vid ett litet antal register tvivel om en snabb utveckling av kvantdatorer som verkligen fungerar. Trots detta kan man inte förneka att stora framsteg gjorts inom kvantteknologin. Det råder givetvis ett stort gap mellan skapandet av en leksakskvantdator med 10-15 kvantregister och att t.ex. tillgodose de tekniska förutsättningarna för det projekt på 100 kvantregister som aviserades för några år sen i USA. Det är också uppenbart att svårigheterna ökar ickelinjärt med ökningen av antalet register. Därför är simulering av kvantdatorer i klassiska datorer en viktig del av kvantdatorprojektet. Självklart kan man inte förvänta sig att en kvantalgoritm skall lösa ett NP-problem i polynomisk tid i en klassisk dator. Detta är heller inte syftet med klassisk simulering. Den klassiska simuleringen av kvantdatorer kommer att täcka en del av gapet mellan den teoretiskt matematiska formuleringen av kvantmekaniken och ett förverkligande av en kvantdator. Ett av de viktigaste problemen i vetenskapen om kvantdatorn är att utveckla ett nytt symboliskt språk för kvantdatorerna och att anpassa redan existerande symboliska språk för klassiska datorer till kvantalgoritmer. Denna avhandling ägnas åt en anpassning av det symboliska språket Mathematica till kända kvantalgoritmer och motsvarande simulering i klassiska datorer. Konkret kommer vi att representera Simons algoritm, Deutsch-Joszas algoritm, Grovers algoritm, Shors algoritm och kvantfelrättande koder i det symboliska språket Mathematica. Vi använder samma stomme i alla dessa algoritmer. Denna stomme representerar de karaktäristiska egenskaperna i det symboliska språkets framställning av kvantdatorn och det är enkelt att inkludera denna stomme i framtida algoritmer.
Quantum computing is an extremely promising project combining theoretical and experimental quantum physics, mathematics, quantum information theory and computer science. At the first stage of development of quantum computing the main attention was paid to creating a few algorithms which might have applications in the future, clarifying fundamental questions and developing experimental technologies for toy quantum computers operating with a few quantum bits. At that time expectations of quick progress in the quantum computing project dominated in the quantum community. However, it seems that such high expectations were not totally justified. Numerous fundamental and technological problems such as the decoherence of quantum bits and the instability of quantum structures even with a small number of registers led to doubts about a quick development of really working quantum computers. Although it can not be denied that great progress had been made in quantum technologies, it is clear that there is still a huge gap between the creation of toy quantum computers with 10-15 quantum registers and, e.g., satisfying the technical conditions of the project of 100 quantum registers announced a few years ago in the USA. It is also evident that difficulties increase nonlinearly with an increasing number of registers. Therefore the simulation of quantum computations on classical computers became an important part of the quantum computing project. Of course, it can not be expected that quantum algorithms would help to solve NP problems for polynomial time on classical computers. However, this is not at all the aim of classical simulation. Classical simulation of quantum computations will cover part of the gap between the theoretical mathematical formulation of quantum mechanics and the realization of quantum computers. One of the most important problems in "quantum computer science" is the development of new symbolic languages for quantum computing and the adaptation of existing symbolic languages for classical computing to quantum algorithms. The present thesis is devoted to the adaptation of the Mathematica symbolic language to known quantum algorithms and corresponding simulation on the classical computer. Concretely we shall represent in the Mathematica symbolic language Simon's algorithm, the Deutsch-Josza algorithm, Grover's algorithm, Shor's algorithm and quantum error-correcting codes. We shall see that the same framework can be used for all these algorithms. This framework will contain the characteristic property of the symbolic language representation of quantum computing and it will be a straightforward matter to include this framework in future algorithms.
Maciel, Cristiano Baptista Faria. "A memetic algorithm for logistics network design problems." Master's thesis, Instituto Superior de Economia e Gestão, 2014. http://hdl.handle.net/10400.5/8601.
Full textNeste trabalho, um algoritmo memético é desenvolvido com o intuito de ser aplicado a uma rede logística, com três níveis, múltiplos períodos, seleção do meio de transporte e com recurso a outsourcing. O algoritmo memético pode ser aplicado a uma rede logística existente, no sentido de otimizar a sua configuração ou, se necessário, pode ser utilizado para criar uma rede logística de raiz. A produção pode ser internalizada e é permitido o envio direto de produtos para os clientes. Neste problema, as capacidades das diferentes infraestruturas podem ser expandidas ao longo do período temporal. Caso se trate uma infraestrutura já existente, após uma expansão, já não pode ser encerrada. Sempre que se abre uma nova infraestrutura, a mesma também não pode ser encerrada. A heurística é capaz de determinar o número e localizações das infraestrutura a operar, as capacidades e o fluxo de mercadoria na rede logística.
This thesis describes a memetic algorithm applied to the design of a three-echelon logistics network over multiple periods with transportation mode selection and outsourcing. The memetic algorithm can be applied to an existing supply chain in order to obtain an optimized configuration or, if required, it can be used to define a new logistics network. In addition, production can be outsourced and direct shipments of products to customer zones are possible. In this problem, the capacity of an existing or new facility can be expanded over the time horizon. In this case, the facility cannot be closed. Existing facilities, once closed, cannot be reopened. New facilities cannot be closed, once opened. The heuristic is able to determine the number and locations of facilities (i.e. plants and warehouses), capacity levels as well as the flow of products throughout the supply chain.
Dementiev, Roman. "Algorithm engineering for large data sets hardware, software, algorithms." Saarbrücken VDM, Müller, 2006. http://d-nb.info/986494429/04.
Full textDementiev, Roman. "Algorithm engineering for large data sets : hardware, software, algorithms /." Saarbrücken : VDM-Verl. Dr. Müller, 2007. http://deposit.d-nb.de/cgi-bin/dokserv?id=3029033&prov=M&dok_var=1&dok_ext=htm.
Full textKhungurn, Pramook. "Shirayanagi-Sweedler algebraic algorithm stabilization and polynomial GCD algorithms." Thesis, Massachusetts Institute of Technology, 2007. http://hdl.handle.net/1721.1/41662.
Full textIncludes bibliographical references (p. 71-72).
Shirayanagi and Sweedler [12] proved that a large class of algorithms on the reals can be modified slightly so that they also work correctly on floating-point numbers. Their main theorem states that, for each input, there exists a precision, called the minimum converging precision (MCP), at and beyond which the modified "stabilized" algorithm follows the same sequence of steps as the original "exact" algorithm. In this thesis, we study the MCP of two algorithms for finding the greatest common divisor of two univariate polynomials with real coefficients: the Euclidean algorithm, and an algorithm based on QR-factorization. We show that, if the coefficients of the input polynomials are allowed to be any computable numbers, then the MCPs of the two algorithms are not computable, implying that there are no "simple" bounding functions for the MCP of all pairs of real polynomials. For the Euclidean algorithm, we derive upper bounds on the MCP for pairs of polynomials whose coefficients are members of Z, 0, Z[6], and Q[6] where ( is a real algebraic integer. The bounds are quadratic in the degrees of the input polynomials or worse. For the QR-factorization algorithm, we derive a bound on the minimal precision at and beyond which the stabilized algorithm gives a polynomial with the same degree as that of the exact GCD, and another bound on the the minimal precision at and beyond which the algorithm gives a polynomial with the same support as that of the exact GCD. The bounds are linear in (1) the degree of the polynomial and (2) the sum of the logarithm of diagonal entries of matrix R in the QR factorization of the Sylvester matrix of the input polynomials.
by Pramook Khungurn.
M.Eng.
Johansson, Björn, and Emil Österberg. "Algorithms for Large Matrix Multiplications : Assessment of Strassen's Algorithm." Thesis, KTH, Skolan för teknikvetenskap (SCI), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-230742.
Full textStrassen’s algorithm was one of the breakthroughs in matrix analysis in 1968. In this report the thesis of Volker Strassen’s algorithm for matrix multipli- cations along with theories about precisions will be shown. The benefits of using this algorithm compared to naive matrix multiplication and its implica- tions, how its performance compare to the naive algorithm, will be displayed. Strassen’s algorithm will also be assessed on how the output differ when the matrix sizes grow larger, as well as how the theoretical complexity of the al- gorithm differs from the achieved complexity. The studies found that Strassen’s algorithm outperformed the naive matrix multiplication at matrix sizes 1024 1024 and above. The achieved complex- ity was a little higher compared to Volker Strassen’s theoretical. The optimal precision for this case were the double precision, Float64. How the algorithm is implemented in code matters for its performance. A number of techniques need to be considered in order to improve Strassen’s algorithm, optimizing its termination criterion, the manner by which it is padded in order to make it more usable for recursive application and the way it is implemented e.g. parallel computing. Even tough it could be proved that Strassen’s algorithm outperformed the Naive after reaching a certain matrix size, it is still not the most efficient one; e.g. as shown with Strassen-Winograd. One need to be careful of how the sub-matrices are being allocated, to not use unnecessary memory. For further reading one can study cache-oblivious and cache-aware algorithms.
Čápek, Pavel. "Srovnání nástrojů pro animaci algoritmů." Master's thesis, Vysoká škola ekonomická v Praze, 2012. http://www.nusl.cz/ntk/nusl-192639.
Full textRafique, Abid. "Communication optimization in iterative numerical algorithms : an algorithm-architecture interaction." Thesis, Imperial College London, 2014. http://hdl.handle.net/10044/1/17837.
Full textBooks on the topic "Algorithm"
Müller-Hannemann, Matthias, and Stefan Schirra. Algorithm engineering: Bridging the gap between algorithm theory and practice. Berlin: Springer, 2010.
Find full textFeldbauer, Martin. Martin Feldbauer: Allmächtiger Algorithmus = Almighty algorithm. Syke: Syker Vorwerk, 2017.
Find full textSouravlias, Dimitris, Konstantinos E. Parsopoulos, Ilias S. Kotsireas, and Panos M. Pardalos. Algorithm Portfolios. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-68514-0.
Full textMüller-Hannemann, Matthias, and Stefan Schirra, eds. Algorithm Engineering. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-14866-8.
Full textVitter, Jeffrey S., and Christos D. Zaroliagis, eds. Algorithm Engineering. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/3-540-48318-7.
Full textKliemann, Lasse, and Peter Sanders, eds. Algorithm Engineering. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-49487-6.
Full textBrodal, Gerth Stølting, Daniele Frigioni, and Alberto Marchetti-Spaccamela, eds. Algorithm Engineering. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-44688-5.
Full textNäher, Stefan, and Dorothea Wagner, eds. Algorithm Engineering. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-44691-5.
Full textTan, Ying. Fireworks Algorithm. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-46353-6.
Full textSwann, S. Andrew. Zimmerman's algorithm. New York, NY: Daw Books, Inc., 2000.
Find full textBook chapters on the topic "Algorithm"
Bez, Helmut, and Tony Croft. "Quantum algorithms 2: Simon's algorithm." In Quantum Computation, 333–42. Boca Raton: Chapman and Hall/CRC, 2023. http://dx.doi.org/10.1201/9781003264569-23.
Full textBansal, Jagdish Chand, Prathu Bajpai, Anjali Rawat, and Atulya K. Nagar. "Conclusion and Further Research Directions." In Sine Cosine Algorithm for Optimization, 105–6. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-9722-8_6.
Full textBansal, Jagdish Chand, Prathu Bajpai, Anjali Rawat, and Atulya K. Nagar. "Sine Cosine Algorithm." In Sine Cosine Algorithm for Optimization, 15–33. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-9722-8_2.
Full textTaillard, Éric D. "Elements of Graphs and Complexity Theory." In Design of Heuristic Algorithms for Hard Optimization, 3–29. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-13714-3_1.
Full textNassehi, Aydin. "Algorithm." In CIRP Encyclopedia of Production Engineering, 1–6. Berlin, Heidelberg: Springer Berlin Heidelberg, 2018. http://dx.doi.org/10.1007/978-3-642-35950-7_16769-1.
Full textShekhar, Shashi, and Hui Xiong. "Algorithm." In Encyclopedia of GIS, 19. Boston, MA: Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-35973-1_46.
Full textGass, Saul I., and Carl M. Harris. "algorithm." In Encyclopedia of Operations Research and Management Science, 790. New York, NY: Springer US, 2001. http://dx.doi.org/10.1007/1-4020-0611-x_1013.
Full textGass, Saul I., and Carl M. Harris. "algorithm." In Encyclopedia of Operations Research and Management Science, 243. New York, NY: Springer US, 2001. http://dx.doi.org/10.1007/1-4020-0611-x_284.
Full textGass, Saul I., and Carl M. Harris. "algorithm." In Encyclopedia of Operations Research and Management Science, 275. New York, NY: Springer US, 2001. http://dx.doi.org/10.1007/1-4020-0611-x_316.
Full textGass, Saul I., and Carl M. Harris. "algorithm." In Encyclopedia of Operations Research and Management Science, 431. New York, NY: Springer US, 2001. http://dx.doi.org/10.1007/1-4020-0611-x_494.
Full textConference papers on the topic "Algorithm"
Borjesson, Fredrik, and Katja Hölttä-Otto. "Improved Clustering Algorithm for Design Structure Matrix." In ASME 2012 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/detc2012-70076.
Full textEl-Mihoub, Tarek A., Christoph Tholen, and Lars Nolle. "A Simple Algorithm Selector for Continuous Optimisation Problems." In 36th ECMS International Conference on Modelling and Simulation. ECMS, 2022. http://dx.doi.org/10.7148/2022-0099.
Full textDegroote, Hans. "Online Algorithm Selection." In Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/746.
Full textEggensperger, Katharina, Marius Lindauer, and Frank Hutter. "Neural Networks for Predicting Algorithm Runtime Distributions." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/200.
Full textGuo, Lei, Lijian Zhou, Shaohui Jia, Li Yi, Haichong Yu, and Xiaoming Han. "An Automatic Segmentation Algorithm Used in Pipeline Integrity Alignment Sheet Design." In 2010 8th International Pipeline Conference. ASMEDC, 2010. http://dx.doi.org/10.1115/ipc2010-31036.
Full textHuang, Fuxin, Lijue Wang, and Chi Yang. "Ship Hull Form Optimization Using Artificial Bee Colony Algorithm." In SNAME Maritime Convention. SNAME, 2014. http://dx.doi.org/10.5957/smc-2014-t47.
Full textMurukesh, Aadhithya, and R. Logeshwari. "Rock, Paper and Scissor Using AI- Random Forest Algorithm." In International Research Conference on IOT, Cloud and Data Science. Switzerland: Trans Tech Publications Ltd, 2023. http://dx.doi.org/10.4028/p-udj0cq.
Full textHulicki, Maciej. "ALGORITHM TRANSPARENCY AS A SINE QUA NON PREREQUISITE FOR A SUSTAINABLE COMPETITION IN A DIGITAL MARKET?" In International Jean Monnet Module Conference of EU and Comparative Competition Law Issues "Competition Law (in Pandemic Times): Challenges and Reforms. Faculty of Law, Josip Juraj Strossmayer University of Osijek, 2021. http://dx.doi.org/10.25234/eclic/18823.
Full textJesus, Alexandre D., Arnaud Liefooghe, Bilel Derbel, and Luís Paquete. "Algorithm selection of anytime algorithms." In GECCO '20: Genetic and Evolutionary Computation Conference. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3377930.3390185.
Full textKnobloch, Roman, and Jaroslav Mlynek. "Differential Evolution Algorithm In Models Of Technical Optimization." In 35th ECMS International Conference on Modelling and Simulation. ECMS, 2021. http://dx.doi.org/10.7148/2021-0179.
Full textReports on the topic "Algorithm"
Gubaydullina, Zulian, Jan René Judek, Marco Lorenz, and Markus Spiwoks. Gestaltungswille und Algorithm Aversion – Die Auswirkungen der Einflussnahme im Prozess der algorithmischen Entscheidungsfindung auf die Algorithm Aversion. Sonderforschungsgruppe Instituionenanalyse, June 2021. http://dx.doi.org/10.46850/sofia.9783941627925.
Full textFiliz, Ibrahim, Jan René Judek, Marco Lorenz, and Markus Spiwoks. Die Tragik der Algorithm Aversion. Sonderforschungsgruppe Institutionenanalyse, 2021. http://dx.doi.org/10.46850/sofia.9783941627888.
Full textLewis, Dustin, Naz Modirzadeh, and Gabriella Blum. War-Algorithm Accountability. Harvard Law School Program on International Law and Armed Conflict, August 2016. http://dx.doi.org/10.54813/fltl8789.
Full textJudek, Jan René. Die Bereitschaft zur Nutzung von Algorithmen variiert mit der sozialen Information über die schwache vs. starke Akzeptanz: Eine experimentelle Studie zur Algorithm Aversion. Sonderforschungsgruppe Institutionenanalyse, 2022. http://dx.doi.org/10.46850/sofia.9783947850037.
Full textMarty, Frédéric, and Thierry Warin. Deciphering Algorithmic Collusion: Insights from Bandit Algorithms and Implications for Antitrust Enforcement. CIRANO, December 2023. http://dx.doi.org/10.54932/iwpg7510.
Full textLorenz, Markus. Auswirkungen des Decoy-Effekts auf die Algorithm Aversion. Sonderforschungsgruppe Institutionenanalyse, 2022. http://dx.doi.org/10.46850/sofia.9783947850013.
Full textJohansen, Richard A., Christina L. Saltus, Molly K. Reif, and Kaytee L. Pokrzywinski. A Review of Empirical Algorithms for the Detection and Quantification of Harmful Algal Blooms Using Satellite-Borne Remote Sensing. U.S. Army Engineer Research and Development Center, June 2022. http://dx.doi.org/10.21079/11681/44523.
Full textChamplin, Craig, and John P. H. Steele. DTPH56-14H-CAP06 Pipeline Assessment through 4-Dimensional Anomaly Detection and Characterization. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), December 2016. http://dx.doi.org/10.55274/r0011766.
Full textFiliz, Ibrahim, Jan René Judek, Marco Lorenz, and Markus Spiwoks. Reduzierung der Algorithm Aversion durch Erfahrung. Sonderforschungsgruppe Institutionenanalyse, 2021. http://dx.doi.org/10.46850/sofia.9783941627864.
Full textBaader, Franz, and Rafael Peñaloza. Axiom Pinpointing in General Tableaux. Aachen University of Technology, 2007. http://dx.doi.org/10.25368/2022.159.
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