Academic literature on the topic 'Computer algorithms'
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Journal articles on the topic "Computer algorithms"
Ataeva, Gulsina Isroilovna, and Lola Dzhalolovna Yodgorova. "METHODS AND ALGORITHMS OF COMPUTER GRAPHICS." Scientific Reports of Bukhara State University 4, no. 1 (February 26, 2020): 43–47. http://dx.doi.org/10.52297/2181-1466/2020/4/1/3.
Full textXu, Zheng Guang, Chen Chen, and Xu Hong Liu. "An Efficient View-Point Invariant Detector and Descriptor." Advanced Materials Research 659 (January 2013): 143–48. http://dx.doi.org/10.4028/www.scientific.net/amr.659.143.
Full textCropper, Andrew. "The Automatic Computer Scientist." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 13 (June 26, 2023): 15434. http://dx.doi.org/10.1609/aaai.v37i13.26801.
Full textMoosakhah, Fatemeh, and Amir Massoud Bidgoli. "Congestion Control in Computer Networks with a New Hybrid Intelligent Algorithm." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 13, no. 8 (August 23, 2014): 4688–706. http://dx.doi.org/10.24297/ijct.v13i8.7068.
Full textPelter, Michele M., and Mary G. Carey. "ECG Computer Algorithms." American Journal of Critical Care 17, no. 6 (November 1, 2008): 581–82. http://dx.doi.org/10.4037/ajcc2008.17.6.581.
Full textKaltofen, E. "Computer Algebra Algorithms." Annual Review of Computer Science 2, no. 1 (June 1987): 91–118. http://dx.doi.org/10.1146/annurev.cs.02.060187.000515.
Full textRakhimov, Bakhtiyar Saidovich, Feroza Bakhtiyarovna Rakhimova, Sabokhat Kabulovna Sobirova, Furkat Odilbekovich Kuryazov, and Dilnoza Boltabaevna Abdirimova. "Review And Analysis Of Computer Vision Algorithms." American Journal of Applied sciences 03, no. 05 (May 31, 2021): 245–50. http://dx.doi.org/10.37547/tajas/volume03issue05-39.
Full textSchlingemann, D. "Cluster states, algorithms and graphs." Quantum Information and Computation 4, no. 4 (July 2004): 287–324. http://dx.doi.org/10.26421/qic4.4-4.
Full textHandayani, Dwipa, and Abrar Hiswara. "KAMUS ISTILAH ILMU KOMPUTER DENGAN ALGORITMA BOYER MOORE BERBASIS WEB." Jurnal Informatika 19, no. 2 (December 26, 2019): 90–97. http://dx.doi.org/10.30873/ji.v19i2.1519.
Full textBunin, Y. V., E. V. Vakulik, R. N. Mikhaylusov, V. V. Negoduyko, K. S. Smelyakov, and O. V. Yasinsky. "Estimation of lung standing size with the application of computer vision algorithms." Experimental and Clinical Medicine 89, no. 4 (December 17, 2020): 87–94. http://dx.doi.org/10.35339/ekm.2020.89.04.13.
Full textDissertations / Theses on the topic "Computer algorithms"
Mosca, Michele. "Quantum computer algorithms." Thesis, University of Oxford, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.301184.
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.
Rhodes, Daniel Thomas. "Hardware accelerated computer graphics algorithms." Thesis, Nottingham Trent University, 2008. http://irep.ntu.ac.uk/id/eprint/201/.
Full textMims, Mark McGrew. "Dynamical stability of quantum algorithms /." Full text (PDF) from UMI/Dissertation Abstracts International, 2000. http://wwwlib.umi.com/cr/utexas/fullcit?p3004342.
Full textLi, Quan Ph D. Massachusetts Institute of Technology. "Algorithms and algorithmic obstacles for probabilistic combinatorial structures." Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/115765.
Full textCataloged from PDF version of thesis.
Includes bibliographical references (pages 209-214).
We study efficient average-case (approximation) algorithms for combinatorial optimization problems, as well as explore the algorithmic obstacles for a variety of discrete optimization problems arising in the theory of random graphs, statistics and machine learning. In particular, we consider the average-case optimization for three NP-hard combinatorial optimization problems: Large Submatrix Selection, Maximum Cut (Max-Cut) of a graph and Matrix Completion. The Large Submatrix Selection problem is to find a k x k submatrix of an n x n matrix with i.i.d. standard Gaussian entries, which has the largest average entry. It was shown in [13] using non-constructive methods that the largest average value of a k x k submatrix is 2(1 + o(1) [square root] log n/k with high probability (w.h.p.) when k = O(log n/ log log n). We show that a natural greedy algorithm called Largest Average Submatrix LAS produces a submatrix with average value (1+ o(1)) [square root] 2 log n/k w.h.p. when k is constant and n grows, namely approximately [square root] 2 smaller. Then by drawing an analogy with the problem of finding cliques in random graphs, we propose a simple greedy algorithm which produces a k x k matrix with asymptotically the same average value (1+o(1) [square root] 2log n/k w.h.p., for k = o(log n). Since the maximum clique problem is a special case of the largest submatrix problem and the greedy algorithm is the best known algorithm for finding cliques in random graphs, it is tempting to believe that beating the factor [square root] 2 performance gap suffered by both algorithms might be very challenging. Surprisingly, we show the existence of a very simple algorithm which produces a k x k matrix with average value (1 + o[subscript]k(1) + o(1))(4/3) [square root] 2log n/k for k = o((log n)¹.⁵), that is, with asymptotic factor 4/3 when k grows. To get an insight into the algorithmic hardness of this problem, and motivated by methods originating in the theory of spin glasses, we conduct the so-called expected overlap analysis of matrices with average value asymptotically (1 + o(1))[alpha][square root] 2 log n/k for a fixed value [alpha] [epsilon] [1, fixed value a E [1, [square root]2]. The overlap corresponds to the number of common rows and common columns for pairs of matrices achieving this value. We discover numerically an intriguing phase transition at [alpha]* [delta]= 5[square root]2/(3[square root]3) ~~ 1.3608.. [epsilon] [4/3, [square root]2]: when [alpha] < [alpha]* the space of overlaps is a continuous subset of [0, 1]², whereas [alpha] = [alpha]* marks the onset of discontinuity, and as a result the model exhibits the Overlap Gap Property (OGP) when [alpha] > [alpha]*, appropriately defined. We conjecture that OGP observed for [alpha] > [alpha]* also marks the onset of the algorithmic hardness - no polynomial time algorithm exists for finding matrices with average value at least (1+o(1)[alpha][square root]2log n/k, when [alpha] > [alpha]* and k is a growing function of n. Finding a maximum cut of a graph is a well-known canonical NP-hard problem. We consider the problem of estimating the size of a maximum cut in a random Erdős-Rényi graph on n nodes and [cn] edges. We establish that the size of the maximum cut normalized by the number of nodes belongs to the interval [c/2 + 0.47523[square root]c,c/2 + 0.55909[square root]c] w.h.p. as n increases, for all sufficiently large c. We observe that every maximum size cut satisfies a certain local optimality property, and we compute the expected number of cuts with a given value satisfying this local optimality property. Estimating this expectation amounts to solving a rather involved multi-dimensional large deviations problem. We solve this underlying large deviation problem asymptotically as c increases and use it to obtain an improved upper bound on the Max-Cut value. The lower bound is obtained by application of the second moment method, coupled with the same local optimality constraint, and is shown to work up to the stated lower bound value c/2 + 0.47523[square root]c. We also obtain an improved lower bound of 1.36000n on the Max-Cut for the random cubic graph or any cubic graph with large girth, improving the previous best bound of 1.33773n. Matrix Completion is the problem of reconstructing a rank-k n x n matrix M from a sampling of its entries. We propose a new matrix completion algorithm using a novel sampling scheme based on a union of independent sparse random regular bipartite graphs. We show that under a certain incoherence assumption on M and for the case when both the rank and the condition number of M are bounded, w.h.p. our algorithm recovers an [epsilon]-approximation of M in terms of the Frobenius norm using O(nlog² (1/[epsilon])) samples and in linear time O(nlog² (1/[epsilon])). This provides the best known bounds both on the sample complexity and computational cost for reconstructing (approximately) an unknown low-rank matrix. The novelty of our algorithm is two new steps of thresholding singular values and rescaling singular vectors in the application of the "vanilla" alternating minimization algorithm. The structure of sparse random regular graphs is used heavily for controlling the impact of these regularization steps.
by Quan Li.
Ph. D.
Tran, Chan-Hung. "Fast clipping algorithms for computer graphics." Thesis, University of British Columbia, 1986. http://hdl.handle.net/2429/26336.
Full textApplied Science, Faculty of
Electrical and Computer Engineering, Department of
Graduate
Viloria, John A. (John Alexander) 1978. "Optimizing clustering algorithms for computer vision." Thesis, Massachusetts Institute of Technology, 2001. http://hdl.handle.net/1721.1/86847.
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.
O'Brien, Neil. "Algorithms for scientific computing." Thesis, University of Southampton, 2012. https://eprints.soton.ac.uk/355716/.
Full textNofal, Samer. "Algorithms for argument systems." Thesis, University of Liverpool, 2013. http://livrepository.liverpool.ac.uk/12173/.
Full textBooks on the topic "Computer algorithms"
Horowitz, Ellis. Computer algorithms. 2nd ed. Summit, NJ: Silicon Press, 2008.
Find full textHorowitz, Ellis. Computer algorithms. New York: Computer Science Press, 1997.
Find full textHorowitz, Ellis. Computer algorithms. 2nd ed. Summit, NJ: Silicon Press, 2008.
Find full textHorowitz, Ellis. Computer algorithms. 2nd ed. Summit, NJ: Silicon Press, 2008.
Find full textHorowitz, Ellis. Computer algorithms. 2nd ed. Summit, NJ: Silicon Press, 2008.
Find full textHorowitz, Ellis. Computer algorithms. New York: Computer Science Press, 1998.
Find full textBaase, Sara. Computer algorithms: Introduction to design and analysis. 2nd ed. Reading, Mass: Addison-Wesley Pub. Co., 1991.
Find full textBaase, Sara. Computer algorithms: Introduction to design and analysis. 2nd ed. Reading, Mass: Addison-Wesley Pub. Co., 1988.
Find full textSalander, Elisabeth C., and Elisabeth C. Salander. Computer search algorithms. Hauppauge, N.Y: Nova Science Publishers, 2010.
Find full textHorowitz, Ellis. Computer algorithms/C++. 2nd ed. Summit, NJ: Silicon Press, 2008.
Find full textBook chapters on the topic "Computer algorithms"
Phan, Vinhthuy. "Algorithms, Computer." In Encyclopedia of Sciences and Religions, 71–74. Dordrecht: Springer Netherlands, 2013. http://dx.doi.org/10.1007/978-1-4020-8265-8_1476.
Full textZobel, Justin. "Algorithms." In Writing for Computer Science, 115–28. London: Springer London, 2004. http://dx.doi.org/10.1007/978-0-85729-422-7_7.
Full textZobel, Justin. "Algorithms." In Writing for Computer Science, 145–55. London: Springer London, 2014. http://dx.doi.org/10.1007/978-1-4471-6639-9_10.
Full textLim, Daniel. "Algorithms." In Philosophy through Computer Science, 22–29. New York: Routledge, 2023. http://dx.doi.org/10.4324/9781003271284-3.
Full textBaratz, Alan, Inder Gopal, and Adrian Segall. "Fault tolerant queries in computer networks." In Distributed Algorithms, 30–40. Berlin, Heidelberg: Springer Berlin Heidelberg, 1988. http://dx.doi.org/10.1007/bfb0019792.
Full textRoosta, Seyed H. "Computer Architecture." In Parallel Processing and Parallel Algorithms, 1–56. New York, NY: Springer New York, 2000. http://dx.doi.org/10.1007/978-1-4612-1220-1_1.
Full textMehlhorn, Kurt. "The Physarum Computer." In WALCOM: Algorithms and Computation, 8. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-19094-0_3.
Full textErciyes, K. "Algorithms." In Undergraduate Topics in Computer Science, 41–61. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-61115-6_3.
Full textSrivastav, Anand, Axel Wedemeyer, Christian Schielke, and Jan Schiemann. "Algorithms for Big Data Problems in de Novo Genome Assembly." In Lecture Notes in Computer Science, 229–51. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-21534-6_13.
Full textSutinen, Erkki, and Matti Tedre. "ICT4D: A Computer Science Perspective." In Algorithms and Applications, 221–31. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-12476-1_16.
Full textConference papers on the topic "Computer algorithms"
Efimov, Aleksey Igorevich, and Dmitry Igorevich Ustukov. "Comparative Analysis of Stereo Vision Algorithms Implementation on Various Architectures." In 32nd International Conference on Computer Graphics and Vision. Keldysh Institute of Applied Mathematics, 2022. http://dx.doi.org/10.20948/graphicon-2022-484-489.
Full textSpector, Lee. "Evolving quantum computer algorithms." In the 11th annual conference companion. New York, New York, USA: ACM Press, 2009. http://dx.doi.org/10.1145/1570256.1570420.
Full textSpector, Lee. "Evolving quantum computer algorithms." In the 13th annual conference companion. New York, New York, USA: ACM Press, 2011. http://dx.doi.org/10.1145/2001858.2002128.
Full textMilne, Darran. "Computer-Generated Holography Algorithms." In Frontiers in Optics. Washington, D.C.: Optica Publishing Group, 2023. http://dx.doi.org/10.1364/fio.2023.fm1a.4.
Full textFreeman, William T. "Where computer vision needs help from computer science." In Proceedings of the Twenty-Second Annual ACM-SIAM Symposium on Discrete Algorithms. Philadelphia, PA: Society for Industrial and Applied Mathematics, 2011. http://dx.doi.org/10.1137/1.9781611973082.64.
Full textKosovskaya, Tatiana, and Juan Zhou. "Algorithms for Checking Isomorphism of Two Elementary Conjunctiоns." In Computer Science and Information Technologies 2023. Institute for Informatics and Automation Problems, 2023. http://dx.doi.org/10.51408/csit2023_01.
Full textE. Fantacci, M., S. Bagnasco, N. Camarlinghi, E. Fiorina, E. Lopez Torres, F. Pennanzio, c. Peroni, et al. "A Web-based Computer Aided Detection System for Automated Search of Lung Nodules in Thoracic Computed Tomography Scans." In International Conference on Bioinformatics Models, Methods and Algorithms. SCITEPRESS - Science and and Technology Publications, 2015. http://dx.doi.org/10.5220/0005280102130218.
Full textCzakoova, Krisztina. "DEVELOPING ALGORITHMIC THINKING BY EDUCATIONAL COMPUTER GAMES." In eLSE 2020. University Publishing House, 2020. http://dx.doi.org/10.12753/2066-026x-20-003.
Full textBulavintsev, Vadim, and Dmitry Zhdanov. "Method for Adaptation of Algorithms to GPU Architecture." In 31th International Conference on Computer Graphics and Vision. Keldysh Institute of Applied Mathematics, 2021. http://dx.doi.org/10.20948/graphicon-2021-3027-930-941.
Full text"Computer aspects of numerical algorithms." In 2008 International Multiconference on Computer Science and Information Technology. IEEE, 2008. http://dx.doi.org/10.1109/imcsit.2008.4747248.
Full textReports on the topic "Computer algorithms"
Poggio, Tomaso, and James Little. Parallel Algorithms for Computer Vision. Fort Belvoir, VA: Defense Technical Information Center, March 1988. http://dx.doi.org/10.21236/ada203947.
Full textLeach, Ronald J. Analysis of Blending Algorithms in Computer Graphics. Fort Belvoir, VA: Defense Technical Information Center, October 1988. http://dx.doi.org/10.21236/ada201921.
Full textDixon, L. C., and R. C. Price. Optimisation Algorithms for Highly Parallel Computer Architectures. Fort Belvoir, VA: Defense Technical Information Center, December 1990. http://dx.doi.org/10.21236/ada235911.
Full textLeach, Ronald J. Analysis of Blending Algorithms in Computer Graphics. Fort Belvoir, VA: Defense Technical Information Center, November 1991. http://dx.doi.org/10.21236/ada244279.
Full textKupinski, Matthew A. Investigation of Genetic Algorithms for Computer-Aided Diagnosis. Fort Belvoir, VA: Defense Technical Information Center, October 2000. http://dx.doi.org/10.21236/ada393995.
Full textSchnabel, R. Concurrent Algorithms for Numerical Computation on Hypercube Computer. Fort Belvoir, VA: Defense Technical Information Center, February 1988. http://dx.doi.org/10.21236/ada195502.
Full textKupinski, Matthew A. Investigation of Genetic Algorithms for Computer-Aided Diagnosis. Fort Belvoir, VA: Defense Technical Information Center, October 1999. http://dx.doi.org/10.21236/ada391457.
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 textVarastehpour, Soheil, Hamid Sharifzadeh, and Iman Ardekani. A Comprehensive Review of Deep Learning Algorithms. Unitec ePress, 2021. http://dx.doi.org/10.34074/ocds.092.
Full textAinsworth, James S., and Steven Kubala. Computer Simulation Modeling: A Method for Predicting the Utilities of Alternative Computer-Aided Treat Evaluation Algorithms. Fort Belvoir, VA: Defense Technical Information Center, September 1990. http://dx.doi.org/10.21236/ada230252.
Full text