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Relevant bibliographies by topics / Symbolic algorithms

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Academic literature on the topic 'Symbolic algorithms'

Author: Grafiati

Published: 24 April 2022

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Journal articles on the topic "Symbolic algorithms"

1

Imam, T., and M. Kaykobad. "Symbolic substitution basedcanonical recoding algorithms." Computers & Mathematics with Applications 48, no. 10-11 (2004): 1541–48. http://dx.doi.org/10.1016/j.camwa.2004.05.008.

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Tseitlin, G. E. "Design of symbolic-processing algorithms." Cybernetics and Systems Analysis 29, no. 2 (1993): 167–76. http://dx.doi.org/10.1007/bf01132777.

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Chidananda Gowda, K., and T. V. Ravi. "Genetic algorithms for symbolic clustering." Sadhana 21, no. 4 (1996): 465–75. http://dx.doi.org/10.1007/bf02745569.

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Askar, S. S., and A. A. Karawia. "On Solving Pentadiagonal Linear Systems via Transformations." Mathematical Problems in Engineering 2015 (2015): 1–9. http://dx.doi.org/10.1155/2015/232456.

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Many authors have studied numerical algorithms for solving the linear systems of pentadiagonal type. The well-known fast pentadiagonal system solver algorithm is an example of such algorithms. The current paper describes new numerical and symbolic algorithms for solving pentadiagonal linear systems via transformations. The proposed algorithms generalize the algorithms presented in El-Mikkawy and Atlan, 2014. Our symbolic algorithms remove the cases where the numerical algorithms fail. The computational cost of our algorithms is better than those algorithms in literature. Some examples are give

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Korabelnikov, Vyacheslav. "Symbolic integration algorithms in CAS MathPartner." Tambov University Reports. Series: Natural and Technical Sciences, no. 125 (2019): 75–89. http://dx.doi.org/10.20310/1810-0198-2019-24-125-75-89.

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Risch theorem, published in 1969, gave beginning to creation of procedure library for symbolic integration. But such library, for past almost 50 years, still not been created. Some attempts of creation such libraries is known, but not one of them not finished. In computer algebra system MathPartner a new procedure library for symbolic integration, based on Risch theorem, is creating. We give detailed description of basic procedures contained in this library, and role of each procedure in symbolic integration algorithm. We represent procedural block diagram of whole algorithm and examples of co

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Shokouhifar, Mohammad, and Ali Jalali. "Automatic Simplified Symbolic Analysis of Analog Circuits Using Modified Nodal Analysis and Genetic Algorithm." Journal of Circuits, Systems and Computers 24, no. 04 (2015): 1550056. http://dx.doi.org/10.1142/s0218126615500565.

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In this paper, a hybrid methodology based on modified nodal analysis (MNA) and genetic algorithm (GA) is presented for simplified symbolic small-signal analysis of analog circuits containing semiconductor devices like MOSFETs. At first, the circuit is analyzed by the MNA, and the derived exact continuous-time transfer function is automatically simplified via GA. We propose a new multi-objective criterion for symbolic simplification of continuous-time transfer functions, which can be performed by such optimization algorithms as local-search algorithms, heuristic algorithms, swarm-intelligence a

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Li, Feng Ying, Tian Long Gu, and Liang Chang. "A Symbolic OBDD-Based Algorithm for Assembly Sequence Planning." Advanced Materials Research 97-101 (March 2010): 2444–48. http://dx.doi.org/10.4028/www.scientific.net/amr.97-101.2444.

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Algorithms based on timed Petri net are competitive for solving the problem of assembly sequence planning (ASP). In order to alleviate the state-space explosion problem which is caused by the Petri net-based representation of assembly sequences and to improve the efficiency of planning algorithms, an approach based on ordinary binary decision diagrams (OBDDs) is presented in this paper. On the one hand, all the timed transitions in Petri nets are substituted by some technically designed timed transition structures; on the other hand, Petri nets used in algorithms of assembly sequence planning

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Korabelnikov, Vyacheslav A. "Procedural interpretation of symbolic integration algorithms in MathPartner system." Tambov University Reports. Series: Natural and Technical Sciences, no. 126 (2019): 166–78. http://dx.doi.org/10.20310/1810-0198-2019-24-126-166-178.

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The work is devoted to the development of a procedure library for the computer algebra system MathPartner. A software implementation of symbolic integration algorithms is being developed. The solution of the problem of symbolic integration is divided into three stages. At the first stage, the integrand is reduced to the form necessary for applying the Rish algorithm. A description is given of the corresponding procedures that reduce the integrand to an expression containing a finite set of arithmetic operations and compositions of logarithmic functions and exponentials, and also make a set of

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Berka, Petr, and Ivan Bruha. "Empirical Comparison of Various Discretization Procedures." International Journal of Pattern Recognition and Artificial Intelligence 12, no. 07 (1998): 1017–32. http://dx.doi.org/10.1142/s0218001498000567.

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The genuine symbolic machine learning (ML) algorithms are capable of processing symbolic, categorial data only. However, real-world problems, e.g. in medicine or finance, involve both symbolic and numerical attributes. Therefore, there is an important issue of ML to discretize (categorize) numerical attributes. There exist quite a few discretization procedures in the ML field. This paper describes two newer algorithms for categorization (discretization) of numerical attributes. The first one is implemented in the KEX (Knowledge EXplorer) as its preprocessing procedure. Its idea is to discretiz

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Astapov, I. S., and N. S. Astapov. "Algorithms for Symbolic Solving of Algebraic Equations." PROGRAMMNAYA INGENERIA 8, no. 9 (2017): 422–32. http://dx.doi.org/10.17587/prin.8.422-432.

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Dissertations / Theses on the topic "Symbolic algorithms"

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Gerhard, Jürgen. "Modular algorithms in symbolic summation and symbolic integration /." Berlin [u.a.] : Springer, 2004. http://www.loc.gov/catdir/toc/fy0801/2004115730.html.

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Coupe, Henry David James. "Non-symbolic fragmentation cryptographic algorithms." Thesis, University of Nottingham, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.423658.

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Nyman, 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.

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<p>Utvecklandet 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 pro

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Richardson, Craig Howard. "The symbolic representation, analysis, and manipulation of morphological algorithms." Diss., Georgia Institute of Technology, 1991. http://hdl.handle.net/1853/13358.

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Lindroth, Olof. "A random formula lower bound for ordered DLL extended with local symmetry recognition /." Uppsala, 2004. http://www.math.uu.se/research/pub/Lindroth1.pdf.

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Hulva, Jiří. "Koevoluční algoritmus pro úlohy založené na testu." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2014. http://www.nusl.cz/ntk/nusl-236019.

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This thesis deals with the usage of coevolution in the task of symbolic regression. Symbolic regression is used for obtaining mathematical formula which approximates the measured data. It can be executed by genetic programming - a method from the category of evolutionary algorithms that is inspired by natural evolutionary processes. Coevolution works with multiple evolutionary processes that are running simultaneously and influencing each other. This work deals with the design and implementation of the application which performs symbolic regression using coevolution on test-based problems. The

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Harris, Jason F. "Core foundations, algorithms, and language design for symbolic computation in physics." Thesis, University of Canterbury. Physics, 1999. http://hdl.handle.net/10092/6073.

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This thesis presents three contributions to the field of symbolic computation, followed by their application to symbolic physics computations. The first contribution is to interfacing systems. The Notation package, which is developed in this thesis, allows the entry and the creation of advanced notations in the Mathematica symbolic computation system. In particular, a complete and functioning notation for both Dirac's BraKet notation as well as a full tensorial notation, are given herein. The second part of the thesis introduces a prototype based rule inheritance language paradigm that is ap

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Qian, Kairong Computer Science &amp Engineering Faculty of Engineering UNSW. "Formal symbolic verification using heuristic search and abstraction techniques." Awarded by:University of New South Wales. School of Computer Science and Engineering, 2006. http://handle.unsw.edu.au/1959.4/25703.

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Computing devices are pervading our everyday life and imposing challenges for designers that have the responsibility of producing reliable hardware and software systems. As systems grow in size and complexity, it becomes increasingly difficult to verify whether a design works as intended. Conventional verification methods, such as simulation and testing, exercise only parts of the system and from these parts, draw conclusions about the correctness of the total design. For complex designs, the parts of the system that can be verified are relatively small. Formal verification aims to overcome th

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Chen, Hsinchun. "Machine Learning for Information Retrieval: Neural Networks, Symbolic Learning, and Genetic Algorithms." Wiley Periodicals, Inc, 1995. http://hdl.handle.net/10150/106427.

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Artificial Intelligence Lab, Department of MIS, University of Arizona<br>Information retrieval using probabilistic techniques has attracted significant attention on the part of researchers in information and computer science over the past few decades. In the 1980s, knowledge-based techniques also made an impressive contribution to “intelligent” information retrieval and indexing. More recently, information science researchers have turned to other newer artificial-intelligence- based inductive learning techniques including neural networks, symbolic learning, and genetic algorithms. These newe

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Askar, Sameh El Said Abdel Aziz. "Symbolic approaches and artificial intelligence algorithms for solving multi-objective optimisation problems." Thesis, Cranfield University, 2011. http://dspace.lib.cranfield.ac.uk/handle/1826/5557.

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Problems that have more than one objective function are of great importance in engineering sciences and many other disciplines. This class of problems are known as multi-objective optimisation problems (or multicriteria). The difficulty here lies in the conflict between the various objective functions. Due to this conflict, one cannot find a single ideal solution which simultaneously satisfies all the objectives. But instead one can find the set of Pareto-optimal solutions (Pareto-optimal set) and consequently the Pareto-optimal front is established. Finding these solutions plays an important

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Books on the topic "Symbolic algorithms"

1

Symbolic ansymptotics. Springer, 2004.

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Gerhard, Jürgen. Modular Algorithms in Symbolic Summation and Symbolic Integration. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/b104035.

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Shackell, John R. Symbolic Asymptotics. Springer Berlin Heidelberg, 2004.

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Symbolic integration I: Transcendental functions. Springer, 1996.

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Symbolic integration I: Transcendental functions. 2nd ed. Springer, 2005.

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Rajan, E. G. Symbolic computing: Signal and image processing. B.S. Publications, 2005.

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Verbruggen, H. B. Fuzzy Algorithms for Control. Springer Netherlands, 1999.

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Uspensky, Vladimir. Algorithms: Main Ideas and Applications. Springer Netherlands, 1993.

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Portela, Artur. Finite Elements Using Maple: A Symbolic Programming Approach. Springer Berlin Heidelberg, 2002.

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Novikov, N. N. Issledovanii͡a︡ po matematicheskoĭ logike i teorii algoritmov =. Izd-vo Tbilisskogo universiteta, 1989.

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Book chapters on the topic "Symbolic algorithms"

1

Shackell, John R. "Algorithms for Function Towers." In Symbolic Asymptotics. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-662-10176-6_5.

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Prakash, Amit, and Adnan Aziz. "Symbolic Model Checking." In Encyclopedia of Algorithms. Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-30162-4_416.

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Aziz, Adnan, and Amit Prakash. "Symbolic Model Checking." In Encyclopedia of Algorithms. Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4939-2864-4_416.

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Aziz, Adnan, and Amit Prakash. "Symbolic Model Checking." In Encyclopedia of Algorithms. Springer US, 2014. http://dx.doi.org/10.1007/978-3-642-27848-8_416-2.

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Traverso, Carlo. "Gröbner trace algorithms." In Symbolic and Algebraic Computation. Springer Berlin Heidelberg, 1989. http://dx.doi.org/10.1007/3-540-51084-2_12.

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Watt, Stephen M. "Algorithms for Symbolic Polynomials." In Computer Algebra in Scientific Computing. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11870814_26.

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Paule, Peter, Bruno Buchberger, Lena Kartashova, Manuel Kauers, Carsten Schneider, and Franz Winkler. "Algorithms in Symbolic Computation." In Hagenberg Research. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-02127-5_2.

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Meinel, Christoph, and Thorsten Theobald. "Symbolic Model Checking." In Algorithms and Data Structures in VLSI Design. Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/978-3-642-58940-9_11.

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Wang, Dongming. "Various elimination algorithms." In Texts and Monographs in Symbolic Computation. Springer Vienna, 2001. http://dx.doi.org/10.1007/978-3-7091-6202-6_5.

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Thakur, Aditya, Matt Elder, and Thomas Reps. "Bilateral Algorithms for Symbolic Abstraction." In Static Analysis. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33125-1_10.

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Conference papers on the topic "Symbolic algorithms"

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Evans, B. L., and J. H. McClellan. "Algorithms for symbolic linear convolution." In Proceedings of 1994 28th Asilomar Conference on Signals, Systems and Computers. IEEE Comput. Soc. Press, 1994. http://dx.doi.org/10.1109/acssc.1994.471600.

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Schmitt, Bruno, Mathias Soeken, and Giovanni De Micheli. "Symbolic Algorithms for Token Swapping." In 2020 IEEE 50th International Symposium on Multiple-Valued Logic (ISMVL). IEEE, 2020. http://dx.doi.org/10.1109/ismvl49045.2020.00-34.

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Schmitt, Bruno, Mathias Soeken, and Giovanni De Micheli. "Symbolic Algorithms for Token Swapping." In 2020 IEEE 50th International Symposium on Multiple-Valued Logic (ISMVL). IEEE, 2020. http://dx.doi.org/10.1109/ismvl49045.2020.00-34.

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Beck, Robert E., and Bernard Kolman. "Symbolic algorithms for Lie algebra computation." In the fifth ACM symposium. ACM Press, 1986. http://dx.doi.org/10.1145/32439.32456.

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Lipitakis, Anastasia-Dimitra, and Sotiris Kotsiantis. "Combining ensembles algorithms of symbolic learners." In 2015 6th International Conference on Information, Intelligence, Systems and Applications (IISA). IEEE, 2015. http://dx.doi.org/10.1109/iisa.2015.7388118.

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Watt, Stephen. "A Review of Algorithms for Symbolic Domains." In 2019 21st International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC). IEEE, 2019. http://dx.doi.org/10.1109/synasc49474.2019.00013.

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Andreica, A., D. Stuparu, and I. Mantu. "Symbolic modelling of database representations." In Seventh International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC'05). IEEE, 2005. http://dx.doi.org/10.1109/synasc.2005.69.

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Bianco, L., V. Manca, and S. Zorzan. "Symbolic representations of biological oscillations." In Seventh International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC'05). IEEE, 2005. http://dx.doi.org/10.1109/synasc.2005.71.

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Carstea, Alexandru, Georgiana Macariu, Marc Frincu, and Dana Petcu. "Workflow Management for Symbolic Grid Services." In 2008 10th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing. IEEE, 2008. http://dx.doi.org/10.1109/synasc.2008.55.

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Abraham, Erika. "Symbolic Computation Techniques in Satisfiability Checking." In 2016 18th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC). IEEE, 2016. http://dx.doi.org/10.1109/synasc.2016.014.

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Reports on the topic "Symbolic algorithms"

1

Oppenheim, Alan V. Numerical and Symbolic Algorithms for Application Specific Signal Processing. Defense Technical Information Center, 1995. http://dx.doi.org/10.21236/ada300362.

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Oppenheim, Alan V. Numerical and Symbolic Algorithms for Application Specific Signal Processing. Defense Technical Information Center, 1993. http://dx.doi.org/10.21236/ada273971.

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Oppenheim, Alan V. Numerical and Symbolic Algorithms for Application Specific Signal Processing. Defense Technical Information Center, 1994. http://dx.doi.org/10.21236/ada288202.

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Oppenheim, Alan V. Numerical and Symbolic Algorithms for Application and Specific Signal Processing. Defense Technical Information Center, 1994. http://dx.doi.org/10.21236/ada279902.

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Loehle, C. S. An algorithm for symbolic dimensional analysis. Office of Scientific and Technical Information (OSTI), 1989. http://dx.doi.org/10.2172/6357117.

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Chauhan, Pankaj, Daniel Kroening, and Edmund Clarke. A SAT-Based Algorithm for Reparameterization in Symbolic Simulation. Defense Technical Information Center, 2003. http://dx.doi.org/10.21236/ada461257.

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