Relevant bibliographies by topics / Cuppen Divide and Conquer

Academic literature on the topic 'Cuppen Divide and Conquer'

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Published: 25 May 2024

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Journal articles on the topic "Cuppen Divide and Conquer"

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Thangavelu, Govindarajan, and Colin C. Anderson. "Divide and conquer." Chimerism 2, no. 1 (January 2011): 29–32. http://dx.doi.org/10.4161/chim.15083.

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Tanenbaum, Leora, Sherrye Henry, Rene Denfeld, and Barbara Findlen. "Divide and Conquer?" Women's Review of Books 12, no. 9 (June 1995): 5. http://dx.doi.org/10.2307/4022083.

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Kester, Grant H. "Divide and Conquer." Afterimage 18, no. 8 (March 1, 1991): 4. http://dx.doi.org/10.1525/aft.1991.18.8.4.

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D'Ambrosia, Robert. "Divide and Conquer." Orthopedics 11, no. 12 (December 1988): 1643. http://dx.doi.org/10.3928/0147-7447-19881201-05.

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Neville-Neil, George V. "Divide and Conquer." Queue 19, no. 3 (June 30, 2021): 37–39. http://dx.doi.org/10.1145/3475965.3477581.

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Bisection is of no use if you have a heisenbug that fails only from time to time. These subtle bugs are the hardest to fix and the ones that cause us to think critically about what we are doing. Timing bugs, bugs in distributed systems, and all the difficult problems we face in building increasingly complex software systems can't yet be addressed by simple bisection. It's often the case that it would take longer to write a usable bisection test for a complex problem than it would to analyze the problem whilst at the tip of the tree.
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Neville-Neil, George V. "Divide and conquer." Communications of the ACM 64, no. 10 (October 2021): 25. http://dx.doi.org/10.1145/3481431.

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Mushtaq, Najum. "Divide and conquer." Bulletin of the Atomic Scientists 63, no. 1 (January 1, 2007): 17–19. http://dx.doi.org/10.2968/063001005.

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Beran, Michael J., Andrew J. Kelly, Bonnie M. Perdue, Will Whitham, Melany Love, Victoria Kelly, and Audrey E. Parrish. "Divide and Conquer." Experimental Psychology 66, no. 4 (July 2019): 296–309. http://dx.doi.org/10.1027/1618-3169/a000454.

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Abstract. We assessed the ability of preschool children to assign the most difficult tasks to a symbolic helper. First, children were taught that a toy “helper” could aid them in remembering the location of a hidden item. Children preferentially assigned the helper to the objectively most difficult locations to remember. Each child then completed eight more tests, assessing a range of different skills such as counting, object identification, and word reading. Children again could assign some stimuli in each task to the helper, leaving the remaining stimuli for themselves to respond to in the given tasks. They were not explicitly told to assign the hardest stimulus to the helper. However, children consistently still did so in most tasks, although some tasks showed an effect of age where older children were more proficient in assigning the objectively more difficult stimuli to the helper. These results highlight a potential form of metacognition in young children in which they can monitor difficulty across varied kinds of assessments and use a generalized tool for asking for help that does not require verbal responding.
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Crow, Diana. "Divide and Conquer." Scientific American 315, no. 2 (July 19, 2016): 14–15. http://dx.doi.org/10.1038/scientificamerican0816-14b.

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Nunes-Alves, Cláudio. "Divide and conquer." Nature Reviews Microbiology 12, no. 12 (November 10, 2014): 794. http://dx.doi.org/10.1038/nrmicro3387.

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Dissertations / Theses on the topic "Cuppen Divide and Conquer"

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Courtois, Jérôme. "Leak study of cryptosystem implementations in randomized RNS arithmetic." Electronic Thesis or Diss., Sorbonne université, 2020. http://www.theses.fr/2020SORUS290.

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On parlera d'analyse forte pour une analyse qui permet de retrouver la clef d'un système cryptographique et d'une analyse faible dans le cas où on élimine des clefs candidates. Le but de cette thèse est essentiellement la compréhension du comportement de l'aléa des distances de Hamming produites par un système cryptographique de type ECC (Elliptic Curve for Cryptography) quand on utilise une représentation RNS (Residue Number System) avec la méthode des moduli aléatoires. Le Chapitre 2 introduit les différentes notions nécessaires à la compréhension de ce document. Il introduit brièvement l'algorithme de multiplication modulaire (Algorithme de Montgomery pour RNS) qui a inspiré la méthode des moduli aléatoires. Puis il décrit l'algorithme qui génère les séquences de distances de Hamming nécessaires à notre analyse. Ensuite il montre quel niveau de résistance apporte la méthode des moduli aléatoires contre différentes attaques classiques comme DPA (Diferential Power Analysis), CPA (Correlation Power Analysis), DPA du second ordre et MIA (Mutual Information Analysis). On apporte une compréhension de la distribution des distances de Hamming considérées comme des variables aléatoires. Suite à cela, on ajoute l'hypothèse gaussienne sur les distances de Hamming. On utilise alors le MLE (Maximum Likelihood Estimator) et une analyse forte comme pour faire des Template Attacks pour avoir une compréhension fine du niveau d'aléa apporté par la méthode des moduli aléatoires. Le Chapitre 3 est une suite naturelle des conclusions apportées par le Chapitre 2 sur l'hypothèse gaussienne. Si des attaques sont possibles avec le MLE, c'est qu'il existe sans doute des relations fortes entre les distances de Hamming considérées comme des variables aléatoires. La Section 3.2 cherche à quantifier ce niveau de dépendance des distances de Hamming. Ensuite, en restant dans l'hypothèse gaussienne, on remarquera qu'il est possible de construire une type de DPA qu'on a appelé DPA square reposant sur la covariance au lieu de la moyenne comme dans la DPA classique. Mais cela reste très gourmand en traces d'observation d'autant que dans de nombreux protocoles utilisant une ECC, on utilise une clef qu'une seule fois. La Section 3.4 s'efforce de montrer qu'il existe de l'information sur peu de traces de distances de Hamming malgré la randomisation des moduli. Pour cela, on fait un MLE par un conditionnement sur l'une des distances de Hamming avec une analyse faible. Le dernier Chapitre 4 commence par introduire brièvement les choix algorithmiques qui ont été faits pour résoudre les problèmes d'inversion de matrices de covariance (symétriques définies positives) de la Section 3.2 et l'analyse des relations fortes entre les Hamming dans la Section 3.2. On utilise ici des outils de Graphics Processing Unit (GPU) sur un très grand nombre de matrices de petites tailles. On parlera de Batch Computing. La méthode LDLt présentée au début de ce chapitre s'est avérée insuffisante pour résoudre complètement le problème du MLE conditionné présenté dans la Section 3.4. On présente le travail sur l'amélioration d'un code de diagonalisation de matrice tridiagonale utilisant le principe de Diviser pour Régner (Divide & Conquer) développé par Lokmane Abbas-Turki et Stéphane Graillat. On présente une généralisation de ce code, des optimisations en temps de calcul et une amélioration de la robustesse des calculs en simple précision pour des matrices de taille inférieure à 32
We will speak of strong analysis for an analysis which makes it possible to find the key to a cryptographic system. We define a weak analysis in the case where candidate keys are eliminated. The goal of this thesis is to understand the behavior of the random of Hamming distances produced by an ECC (Elliptic Curve for Cryptography) cryptographic system when using a RNS (Residue Number System) representation with the random moduli method. Chapter 2 introduces the different concepts for understanding this document. He brieflyintroducesthemodularmultiplicationalgorithm(MontgomeryalgorithmforRNS) which inspired the method of random moduli. Then it describes the algorithm which generatestheHammingdistancesequencesnecessaryforouranalysis. Thenitshowswhat level of resistance brings the method of random moduli against different classic attacks like DPA (Diferrential Power Analysis), CPA (Correlation Power Analysis), DPA of the second order and MIA (Mutual Information Analysis). We provide an understanding of the distribution of Hamming distances considered to be random variables. Following this, we add the Gaussian hypothesis on Hamming distances. We use MLE (Maximum Likelihood Estimator) and a strong analysis as to make Template Attacks to have a fine understanding of the level of random brought by the method of random moduli. The last Chapter 4 begins by briefly introducing the algorithmic choices which have been made to solve the problems of inversion of covariance matrices (symmetric definite positive) of Section 2.5 and the analysis of strong relationships between Hamming in Section 3.2. We use here Graphics Processing Unit (GPU) tools on a very large number of small size matrices. We talk about Batch Computing. The LDLt method presented at the beginning of this chapter proved to be insufficient to completely solve the problem of conditioned MLE presented in Section 3.4. We present work on the improvement of a diagonalization code of a tridiagonal matrix using the principle of Divide & Conquer developed by Lokmane Abbas-Turki and Stéphane Graillat. We present a generalization of this code, optimizations in computation time and an improvement of the accuracy of computations in simple precision for matrices of size lower than 32
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Pantawongdecha, Payut. "Autotuning divide-and-conquer matrix-vector multiplication." Thesis, Massachusetts Institute of Technology, 2016. http://hdl.handle.net/1721.1/105968.

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Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2016.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 73-75).
Divide and conquer is an important concept in computer science. It is used ubiquitously to simplify and speed up programs. However, it needs to be optimized, with respect to parameter settings for example, in order to achieve the best performance. The problem boils down to searching for the best implementation choice on a given set of requirements, such as which machine the program is running on. The goal of this thesis is to apply and evaluate the Ztune approach [14] on serial divide-and-conquer matrix-vector multiplication. We implemented Ztune to autotune serial divide-and-conquer matrix-vector multiplication on machines with different hardware configurations, and found that Ztuneoptimized codes ran 1%-5% faster than the hand-optimized counterparts. We also compared Ztune-optimized results with other matrix-vector multiplication libraries including the Intel Math Kernel Library and OpenBLAS. Since the matrix-vector multiplication problem is a level 2 BLAS, it is not as computationally intensive as level 3 BLAS problems such as matrix-matrix multiplication and stencil computation. As a result, the measurement in matrix-vector multiplication is more prone to error from factors such as noise, cache alignment of the matrix, and cache states, which lead to wrong decision choices for Ztune. We explored multiple options to get more accurate measurements and demonstrated the techniques that remedied these issues. Lastly, we applied the Ztune approach to matrix-matrix multiplication, and we were able to achieve 2%-85% speedup compared to the hand-tuned code. This thesis represents joint work with Ekanathan Palamadai Natarajan.
by Payut Pantawongdecha.
M. Eng.
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Jewell, Sean William. "Divide and conquer sequential Monte Carlo for phylogenetics." Thesis, University of British Columbia, 2015. http://hdl.handle.net/2429/54514.

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Recently reconstructing evolutionary histories has become a computational issue due to the increased availability of genetic sequencing data and relaxations of classical modelling assumptions. This thesis specializes a Divide & conquer sequential Monte Carlo (DCSMC) inference algorithm to phylogenetics to address these challenges. In phylogenetics, the tree structure used to represent evolutionary histories provides a model decomposition used for DCSMC. In particular, speciation events are used to recursively decompose the model into subproblems. Each subproblem is approximated by an independent population of weighted particles, which are merged and propagated to create an ancestral population. This approach provides the flexibility to relax classical assumptions on large trees by parallelizing these recursions.
Science, Faculty of
Statistics, Department of
Graduate
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Piper, Andrew James. "Object-oriented divide-and-conquer for parallel processing." Thesis, University of Cambridge, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.337783.

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Scardillo, Mike, and Mike Nisel. "Divide and Conquer: Improving Post-Flight Data Processing." International Foundation for Telemetering, 1994. http://hdl.handle.net/10150/608595.

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International Telemetering Conference Proceedings / October 17-20, 1994 / Town & Country Hotel and Conference Center, San Diego, California
This paper describes Dryden Flight Research Center's (DFRC's) transition from a mainframe-oriented post-flight data processing system, heavily dependent upon manual operation and scheduling, to a modern, distributed, highly automated system. After developing requirements and a concept development plan, DFRC replaced one multiple-CPU mainframe with five specialized servers, distributing the processing workload and separating functions. Access to flight data was improved by buying and building client server automated retrieval software that takes advantage of the local area network, and by providing over 500 gigabytes of on-line archival storage space. Engineering customers see improved access times and continuous availability (7-days per week, 24-hours per day) of flight research data. A significant reduction in computer operator workload was achieved, and minimal computer operator intervention is now required for flight data retrieval operations. This new post-flight system architecture was designed and built to provide flexibility, extensibility and cost-effective upgradeability. Almost two years of successful operation have proven the viability of the system. Future improvements will focus on decreasing the elapsed time between raw data capture and engineering unit data archival, increasing the on-line archival storage capacity, and decreasing the automated data retrieval response time.
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Khoshfetrat, Pakazad Sina. "Divide and Conquer: Distributed Optimization and Robustness Analysis." Doctoral thesis, Linköpings universitet, Reglerteknik, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-117503.

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As control of large-scale complex systems has become more and more prevalent within control, so has the need for analyzing such controlled systems. This is particularly due to the fact that many of the control design approaches tend to neglect intricacies in such systems, e.g., uncertainties, time delays, nonlinearities, so as to simplify the design procedure. Robustness analysis techniques allow us to assess the effect of such neglected intricacies on performance and stability. Performing robustness analysis commonly requires solving an optimization problem. However, the number of variables of this optimization problem, and hence the computational time, scales badly with the dimension of the system. This limits our ability to analyze large-scale complex systems in a centralized manner. In addition, certain structural constraints, such as privacy requirements or geographical separation, can prevent us from even forming the analysis problem in a centralized manner. In this thesis, we address these issues by exploiting structures that are common in large-scale systems and/or their corresponding analysis problems. This enables us to reduce the computational cost of solving these problems both in a centralized and distributed manner. In order to facilitate distributed solutions, we employ or design tailored distributed optimization techniques. Particularly, we propose three distributed optimization algorithms for solving the analysis problem, which provide superior convergence and/or computational properties over existing algorithms. Furthermore, these algorithms can also be used for solving general loosely coupled optimization problems that appear in a variety of fields ranging from control, estimation and communication systems to supply chain management and economics.
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Yu, Fangqing. "A divide-and-conquer method for 3D capacitance extraction." Thesis, Texas A&M University, 2003. http://hdl.handle.net/1969.1/166.

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This thesis describes a divide-and-conquer algorithm to improve the 3D boundary element method (BEM) for capacitance extraction. We divide large interconnect structures into small sections, set new boundary conditions using the borderfor each section, solve each section, and then combine the results to derive the capacitance. The target application is critical nets where 3D accuracy is required. The new algorithm is a significant improvement over the traditional BEMs and their enhancements, such as the "window" method where conductors far away are dropped, and the "shield" method where conductors hidden behind other conductors are dropped. Experimental results show that our algorithm is 25 times faster than the traditional BEM and 5 times faster than the window+shield method, for medium to large structures. The error of the capacitance computed by the new algorithm is within 2% for self capacitance and 7% for coupling capacitance, compared with the results obtained by solving the entire system using BEM. Furthermore, our algorithms gives accurate distributed RC, where none of the previous 3D BEM algorithms and their enhancements can.
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Moinuddin, Md. "A divide and conquer approach for large spatial dataset." Doctoral thesis, Università degli studi di Padova, 2019. http://hdl.handle.net/11577/3425417.

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In recent times, the rise of `big data' has brought along major computational challenges in all the main disciplines of scientific research, including the field of spatial statistics. Some of these challenges include parametric estimation and quantification of estimation uncertainty that, when building statistical models using big data, pose an important computational load. Many methods have been proposed to address these challenges such as dimension reduction, approximation by Markov random fields, tapering of the covariance matrix, and subsampling based approaches. In this thesis a new \textit{divide-and-conquer} approach is proposed that we call \texttt{farmer} for providing effect size and standard error estimates in spatial models of big data. According to the proposed approach, all observations are divided into blocks that are mutually exclusive according to their position. For each block, the model parameters are estimated and recombined using a fixed or random meta-model to take into account the (possible) spatial dependence. This generalized method can be applied to a wide range of spatial models. For example, consider a linear Gaussian spatial model. In a simulation study, the \texttt{farmer} estimators were compared with estimators based on methods with similar sampling ideas. In the context of the Gaussian model, two applications with real data are presented. The proposed method appears computationally efficient compared to equivalent methods and has lower bias in the estimates. Furthermore, the proposed approach provides a more realistic estimate of standard errors. Finally, we propose an application of the method to generalized linear spatial models for simulated and real counting data.
Negli ultimi due decenni l'avvento dei \textit{big-data} ha portato sfide computazionali in tutte le principali discipline della ricerca scientifica. Anche la Statistica spaziale sta affrontando questa sfida. Quando un modello parametrico viene proposto per \textit{big-data}, la stima parametrica e la quantificazione dell'incertezza nella stima comporta un carico computazionale importante. Per questo sono stati proposti molti metodi per gestire queste sfide quali la riduzione della dimensionalit\`a, l'approssimazione mediante campi casuali di Markov, la rastremazione \textit{tapering} della matrice di covarianza e approcci basati sul campionamento. In questa tesi si propone un nuovo approccio \textit{divide-and-conquer} detto \texttt{farmer} per la stima e la valutazione dell'incertezza dei parametri in modelli spaziali in presenza di grandi moli di dati spaziali. Secondo l'approccio proposto tutte le osservazioni vengono divise in blocchi mutualmente esclusivi secondo la loro posizione e per ogni blocco si stimano i parametri del modello. Le stime vengono quindi ricombinate tramite un meta-modello a effetti fissi o casuali per tenere conto della (eventuale) dipendenza spaziale. Il metodo risulta completamente generale e può essere applicato ad un ampia gamma di modelli spaziali A titolo d'esempio viene considerato un modello spaziale lineare gaussiano. In uno studio di simulazione gli stimatori \texttt{farmer} sono stati confrontati con stimatori che si basano sulla medesima idea di campionamento Sempre nel contesto del modello gaussiano si presentano due applicazioni con dati reali. Il metodo proposto \`{e} risultato computazionalmente efficiente rispetto ai metodi concorrenti, con distorsione delle stime inferiore. Inoltre, l'approccio proposto fornisce una stima pi\`{u} realistica degli errori standard. Infine si propone un'applicazione del metodo a modelli spaziali lineari generalizzati per dati di conteggio simulati e reali.
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Esmaeili, Javad. "Parallel implementation of funtional languages using divide-and-conquer strategies." Thesis, University of Salford, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.308109.

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Simpson, Leonie Ruth. "Divide and conquer attacks on shift register based stream ciphers." Thesis, Queensland University of Technology, 2000.

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Books on the topic "Cuppen Divide and Conquer"

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Cummins, Maureen. Divide & conquer. Rosendale, N.Y.]: [Women's Studio Workshop], 2007.

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Divide & conquer. [Place of publication not identified]: Murray Mcdonald, 2011.

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ill, Mercado Jorge, and Aesop, eds. Divide to conquer. Houston: Advance Pub., 2009.

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Clancy, Tom. Divide and conquer. New York: Simon & Schuster Audio, 2000.

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Copyright Paperback Collection (Library of Congress), ed. Divide and conquer. New York: Berkley Pub. Group, 2000.

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Smith, Diana McLain. Divide or Conquer. New York: Penguin Group (USA), Inc., 2008.

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Clancy, Tom. Divide and conquer. New York: Berkley Books, 2000.

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1947-, Clancy Tom, and Pieczenik Steve, eds. Divide and conquer. London: HarperCollins, 2000.

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Clancy, Tom. Divide and Conquer. New York: Penguin USA, Inc., 2009.

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ill, Mercado Jorge, and Aesop, eds. Divide to conquer =: Divide y venceras. Houston: Advance Pub., 2009.

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Book chapters on the topic "Cuppen Divide and Conquer"

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Al-Haj Baddar, Sherenaz W., and Kenneth E. Batcher. "Divide and Conquer." In Designing Sorting Networks, 43–47. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4614-1851-1_7.

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Freeman, Kassie. "Divide and Conquer." In Community Engagement in Higher Education, 31–39. Rotterdam: SensePublishers, 2015. http://dx.doi.org/10.1007/978-94-6300-007-9_2.

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Cynkin, Thomas M. "Divide and Conquer." In Soviet and American Signalling in the Polish Crisis, 68–108. London: Palgrave Macmillan UK, 1988. http://dx.doi.org/10.1007/978-1-349-09694-7_3.

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Skiena, Steven S. "Divide and Conquer." In Texts in Computer Science, 147–69. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-54256-6_5.

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Shekhar, Shashi, and Hui Xiong. "Divide and Conquer." In Encyclopedia of GIS, 254. Boston, MA: Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-35973-1_321.

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Carpenter, Stanley D. M., Kevin J. Delamer, James R. McIntyre, and Andrew T. Zwilling. "Divide and Conquer." In The War of American Independence, 1763-1783, 99–119. London: Routledge, 2022. http://dx.doi.org/10.4324/9781003041276-7.

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Izadkhah, Habib. "Divide and Conquer." In Problems on Algorithms, 351–400. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-17043-0_10.

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Du, Ding-Zhu, Panos Pardalos, Xiaodong Hu, and Weili Wu. "Divide-and-Conquer." In Introduction to Combinatorial Optimization, 13–41. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-10596-8_2.

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Dixon, Andrew. "Divide and Conquer." In Practical Guide to IT Problem Management, 37–40. Boca Raton: Auerbach Publications, 2022. http://dx.doi.org/10.1201/9781003119975-8.

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Sherine, Anli, Mary Jasmine, Geno Peter, and S. Albert Alexander. "Divide and Conquer." In Algorithm and Design Complexity, 43–73. Boca Raton: CRC Press, 2023. http://dx.doi.org/10.1201/9781003355403-2.

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Conference papers on the topic "Cuppen Divide and Conquer"

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Zhang, Chuanjun, and Bing Xue. "Divide-and-conquer." In the 23rd international conference. New York, New York, USA: ACM Press, 2009. http://dx.doi.org/10.1145/1542275.1542291.

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Lyons, Kathy M., and Ryan Thomas Sharpe. "Divide & conquer." In Proceeding of the 39th ACM annual conference. New York, New York, USA: ACM Press, 2011. http://dx.doi.org/10.1145/2070364.2070368.

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Amato, Ariel, Angel D. Sappa, Alicia Fornés, Felipe Lumbreras, and Josep Lladós. "Divide and conquer." In the 2nd ACM international workshop. New York, New York, USA: ACM Press, 2013. http://dx.doi.org/10.1145/2506364.2506371.

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Nakano, Aiichiro, Shinnosuke Hattori, Rajiv K. Kalia, Weiwei Mou, Ken-ichi Nomura, Pankaj Rajak, Priya Vashishta, et al. "Divide-Conquer-Recombine." In Beowulf '14: Workshop in Honor of Thomas Sterling's 65th Birthday. New York, NY, USA: ACM, 2014. http://dx.doi.org/10.1145/2737909.2737911.

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Shi, Tao, Hui Ma, and Gang Chen. "Divide and conquer." In GECCO '20: Genetic and Evolutionary Computation Conference. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3377929.3389927.

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McDonnell, Tyler, Sari Andoni, Elmira Bonab, Sheila Cheng, Jun-Hwan Choi, Jimmie Goode, Keith Moore, Gavin Sellers, and Jacob Schrum. "Divide and conquer." In GECCO '18: Genetic and Evolutionary Computation Conference. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3205455.3205476.

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Scholz, Ulrich, and Romain Rouvoy. "Divide and conquer." In Ninth international workshop. New York, New York, USA: ACM Press, 2007. http://dx.doi.org/10.1145/1294948.1294958.

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Driff, Lydia Nahla, and Habiba Drias. "Divide and Conquer." In the International Conference. New York, New York, USA: ACM Press, 2018. http://dx.doi.org/10.1145/3230905.3230913.

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Zhang, Zhiwei, Jeffrey Xu Yu, Lu Qin, and Zechao Shang. "Divide & Conquer." In SIGMOD/PODS'15: International Conference on Management of Data. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2723372.2723740.

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Flechais, Ivan, Jens Riegelsberger, and M. Angela Sasse. "Divide and conquer." In the 2005 workshop. New York, New York, USA: ACM Press, 2005. http://dx.doi.org/10.1145/1146269.1146280.

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Reports on the topic "Cuppen Divide and Conquer"

1

Coppersmith, Don, Lisa Fleischer, Bruce Hendrickson, and Ali Pinar. A divide-and-conquer algorithm for identifying strongly connectedcomponents. Office of Scientific and Technical Information (OSTI), March 2003. http://dx.doi.org/10.2172/889876.

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Ainsworth, Paul, and Svetlana Kryukova. A Multimedia Interactive Environment Using Program Archetypes: Divide-and-Conquer. Fort Belvoir, VA: Defense Technical Information Center, January 2006. http://dx.doi.org/10.21236/ada443259.

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Gragg, William, and L. Reichel. A Divide and Conquer Method for Unitary and Orthogonal Eigenproblems. Fort Belvoir, VA: Defense Technical Information Center, February 1989. http://dx.doi.org/10.21236/ada205433.

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Del Carpio, Lucia, Samuel Kapon, and Sylvain Chassang. Using Divide-and-Conquer to Improve Tax Collection: Evidence from the Field. Cambridge, MA: National Bureau of Economic Research, July 2022. http://dx.doi.org/10.3386/w30218.

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Jessup, E. A case against a divide and conquer approach to the nonsymmetric eigenvalue problem. Office of Scientific and Technical Information (OSTI), December 1991. http://dx.doi.org/10.2172/10108206.

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Guan, X., and E. C. Uberbacher. A multiple divide-and-conquer (MDC) algorithm for optimal alignments in linear space. Office of Scientific and Technical Information (OSTI), June 1994. http://dx.doi.org/10.2172/10168027.

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Jessup, E. A case against a divide and conquer approach to the nonsymmetric eigenvalue problem. Office of Scientific and Technical Information (OSTI), December 1991. http://dx.doi.org/10.2172/5926172.

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Borges, Carlos F., and William B. Gragg. A Parallel Divide and Conquer Algorithm for the Generalized Real Symmetric Definite Tridiagonal Eigenproblem. Fort Belvoir, VA: Defense Technical Information Center, December 1992. http://dx.doi.org/10.21236/ada262297.

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Tucker, Jon R., and Rudolph J. Magyar. The potential, limitations, and challenges of divide and conquer quantum electronic structure calculations on energetic materials. Office of Scientific and Technical Information (OSTI), February 2012. http://dx.doi.org/10.2172/1038199.

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