Добірка наукової літератури з теми "Matrices laplaciennes"
Оформте джерело за APA, MLA, Chicago, Harvard та іншими стилями
Зміст
Ознайомтеся зі списками актуальних статей, книг, дисертацій, тез та інших наукових джерел на тему "Matrices laplaciennes".
Біля кожної праці в переліку літератури доступна кнопка «Додати до бібліографії». Скористайтеся нею – і ми автоматично оформимо бібліографічне посилання на обрану працю в потрібному вам стилі цитування: APA, MLA, «Гарвард», «Чикаго», «Ванкувер» тощо.
Також ви можете завантажити повний текст наукової публікації у форматі «.pdf» та прочитати онлайн анотацію до роботи, якщо відповідні параметри наявні в метаданих.
Статті в журналах з теми "Matrices laplaciennes"
Kook, Woong, and Kang-Ju Lee. "Weighted Tree-Numbers of Matroid Complexes." Discrete Mathematics & Theoretical Computer Science DMTCS Proceedings, 27th..., Proceedings (January 1, 2015). http://dx.doi.org/10.46298/dmtcs.2459.
Повний текст джерелаTeufl, Elmar, and Stephan Wagner. "Spanning forests, electrical networks, and a determinant identity." Discrete Mathematics & Theoretical Computer Science DMTCS Proceedings vol. AK,..., Proceedings (January 1, 2009). http://dx.doi.org/10.46298/dmtcs.2699.
Повний текст джерелаMartin, Jeremy L., and Jennifer D. Wagner. "On the Spectra of Simplicial Rook Graphs." Discrete Mathematics & Theoretical Computer Science DMTCS Proceedings vol. AS,..., Proceedings (January 1, 2013). http://dx.doi.org/10.46298/dmtcs.12819.
Повний текст джерелаДисертації з теми "Matrices laplaciennes"
Wehbe, Diala. "Simulations and applications of large-scale k-determinantal point processes." Thesis, Lille 1, 2019. http://www.theses.fr/2019LIL1I012/document.
Повний текст джерелаWith the exponentially growing amount of data, sampling remains the most relevant method to learn about populations. Sometimes, larger sample size is needed to generate more precise results and to exclude the possibility of missing key information. The problem lies in the fact that sampling large number may be a principal reason of wasting time.In this thesis, our aim is to build bridges between applications of statistics and k-Determinantal Point Process(k-DPP) which is defined through a matrix kernel. We have proposed different applications for sampling large data sets basing on k-DPP, which is a conditional DPP that models only sets of cardinality k. The goal is to select diverse sets that cover a much greater set of objects in polynomial time. This can be achieved by constructing different Markov chains which have the k-DPPs as their stationary distribution.The first application consists in sampling a subset of species in a phylogenetic tree by avoiding redundancy. By defining the k-DPP via an intersection kernel, the results provide a fast mixing sampler for k-DPP, for which a polynomial bound on the mixing time is presented and depends on the height of the phylogenetic tree.The second application aims to clarify how k-DPPs offer a powerful approach to find a diverse subset of nodes in large connected graph which authorizes getting an outline of different types of information related to the ground set. A polynomial bound on the mixing time of the proposed Markov chain is given where the kernel used here is the Moore-Penrose pseudo-inverse of the normalized Laplacian matrix. The resulting mixing time is attained under certain conditions on the eigenvalues of the Laplacian matrix. The third one purposes to use the fixed cardinality DPP in experimental designs as a tool to study a Latin Hypercube Sampling(LHS) of order n. The key is to propose a DPP kernel that establishes the negative correlations between the selected points and preserve the constraint of the design which is strictly confirmed by the occurrence of each point exactly once in each hyperplane. Then by creating a new Markov chain which has n-DPP as its stationary distribution, we determine the number of steps required to build a LHS with accordance to n-DPP
Книги з теми "Matrices laplaciennes"
Molitierno, Jason J. Applications of Combinatorial Matrix Theory to Laplacian Matrices of Graphs. Taylor & Francis Group, 2016.
Знайти повний текст джерелаMolitierno, Jason J. Applications of Combinatorial Matrix Theory to Laplacian Matrices of Graphs. Taylor & Francis Group, 2012.
Знайти повний текст джерелаApplications of Combinatorial Matrix Theory to Laplacian Matrices of Graphs. CRC Press LLC, 2012.
Знайти повний текст джерела