Academic literature on the topic 'Matrices laplaciennes'
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Contents
Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Matrices laplaciennes.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Journal articles on the topic "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.
Full textTeufl, 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.
Full textMartin, 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.
Full textDissertations / Theses on the topic "Matrices laplaciennes"
Wehbe, Diala. "Simulations and applications of large-scale k-determinantal point processes." Thesis, Lille 1, 2019. http://www.theses.fr/2019LIL1I012/document.
Full textWith 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
Books on the topic "Matrices laplaciennes"
Molitierno, Jason J. Applications of Combinatorial Matrix Theory to Laplacian Matrices of Graphs. Taylor & Francis Group, 2016.
Find full textMolitierno, Jason J. Applications of Combinatorial Matrix Theory to Laplacian Matrices of Graphs. Taylor & Francis Group, 2012.
Find full textApplications of Combinatorial Matrix Theory to Laplacian Matrices of Graphs. CRC Press LLC, 2012.
Find full text