Différences

Ci-dessous, les différences entre deux révisions de la page.

--- mega:start [2017/10/10 14:10] – Laure DUMAZ
+++ mega:start [2018/02/15 14:45] – Titre et résumé aprem du 15 Mars male
@@ Ligne 8: / Ligne 8: @@
      * Le matin, un mini-cours/GdT/GdR à destination des thésards
      * L'après-midi, deux exposés de recherche de 55min + 10min de questions.
-   * **Lieu.** Institut Henri Poincaré, Paris. Salle variable.
+   * **Lieu.** Institut Henri Poincaré, Paris. Salle 201.
    * **Cadre.** Le GdT est désormais le séminaire officiel associé au [[mega:gdr|GDR MEGA]]. Vous pouvez contacter [[http://www-syscom.univ-mlv.fr/~najim/|Jamal Najim]] pour demander un financement pour vos déplacement au GdT.
    * **Calendrier.** https://calendar.google.com/calendar/ical/qn5qq7dlmp38sc624s4png8umc%40group.calendar.google.com/public/basic.ics
@@ Ligne 17: / Ligne 17: @@
 {{ :mega:20141121_153039.jpg?400 |}}
-===== Exposés à venir 2017-2018 =====
+===== Exposés 2017-2018 =====
 * Vendredi **8 décembre**
-         * 14h30-15h45:  **[[http://perso.ens-lyon.fr/aguionne/|Alice Guionnet]]** //
+         * 10h30-12h00: mini cours par **[[http://www.normalesup.org/~menard/|Laurent Ménard]]** sur la méthode des séries génératrices
-         * 15h45-17h00:  **[[https://www.kcl.ac.uk/nms/depts/mathematics/people/atoz/Fyodorovy.aspx|Yan Fyodorov]]** //On statistics of bi-orthogonal eigenvectors in real and complex Ginibre ensembles combining partial Schur decomposition with supersymmetry.\\ //
+         * 14h30-15h45:  **[[https://www.kcl.ac.uk/nms/depts/mathematics/people/atoz/Fyodorovy.aspx|Yan Fyodorov]]** //On statistics of bi-orthogonal eigenvectors in real and complex Ginibre ensembles combining partial Schur decomposition with supersymmetry.\\ //
- I will present a method of studying the joint probability density (JPD) of an eigenvalue and the associated 'non-orthogonality overlap factor' (also known as the condition number) of the  left and right eigenvectors for non-selfadjoint random matrices. First we derive the general finite size N expression for the JPD of a real eigenvalue and the associated non-orthogonality factor in the real Ginibre ensemble, and then analyze its 'bulk' and 'edge' scaling limits. The ensuing distribution is maximally heavy-tailed, so that all integer moments beyond normalization are divergent. A similar calculation for a complex eigenvalue  and the associated non-orthogonality factor in the complex Ginibre ensemble will be presented as well and yields a distribution with the finite first moment. Its  'bulk' scaling limit yields a distribution whose first moment reproduces the well-known result of Chalker and Mehlig , and I provide the 'edge' scaling distribution for this case as well. The method involves evaluating the ensemble average of products and ratios of integer and half-integer powers of characteristic polynomials for Ginibre matrices, which is performed in the framework of the supersymmetry approach. These results complement recent studies by P. Bourgade & G. Dubach.
+         * 15h45-17h00:  **[[http://perso.ens-lyon.fr/aguionne/|Alice Guionnet]]** //Fluctuations pour les pavages aleatoires et equations de Nekrasov \\ //
 * Vendredi **12 janvier**
+         * 10h30-12h00: mini cours par **[[http://www.camillemale.com|Camille Male]]** sur les méthodes non commutatives en matrices aléatoires
+         * 14h30-15h45:  **[[https://sites.google.com/site/torbenkruegermath/|Torben Krüger]]** //Random matrices with slow correlation decay \\ //
+         * 15h45-17h00:  **[[http://www.iecl.univ-lorraine.fr/~Jeremie.Unterberger/|Jérémie Unterberger]]** //Global fluctuations for 1D log-gas dynamics\\ //
 * Vendredi **9 février**
+         * 10h30-12h00: mini cours par **[[http://romaincouillet.hebfree.org|Romain Couillet]]** //matrices aléatoires et l'apprentissage machine \\ //
+         * 14h00-15h00:  **[[https://perso.univ-rennes1.fr/nizar.demni/Sitenizar/Accueil.html|Nizar Demni]]** //Etats quantiques Browniens et polynome de Jacobi dans le simplexe \\ //
+         * 15h30-16h30:  **[[https://www.lpsm.paris//pageperso/boutil/|Cédric Boutillier]]** //Discrete differential geometry and integrable models on isoradial graphs \\ //
+* Vendredi **16 mars**
+         * 10h30-12h00: mini cours par **[[http://www.proba.jussieu.fr/pageperso/levy/|Thierry Lévy]]**
+         * 14h00-15h00:  **[[http://www.math.ku.dk/~mikosch/|Thomas Mikosch]]** //The largest eigenvalues of the sample covariance matrix in the heavy-tail case\\ //Heavy tails of a time series are typically modeled by power law tails with a positive tail index $\alpha$. We refer to such time series as regularly varying with index $\alpha$. Regular variation of a time series translates into power law tail behavior of the partial sums of the time series above high threshold. This was observed early on in work by A.V. Nagaev (1969) and S.V. Nagaev (1979) who considered sums of iid regularly varying random variables. These results are referred to as heavy-tail or Nagaev-type large deviations. The goal of this lecture is to argue that heavy-tail large deviations are useful tools when dealing with the eigenvalues of the sample covariance matrix of dimension $p\times n$ when $p\to\infty$ as $n\to\infty$ in those cases when one can identify the dominating entries in this matrix. These are the diagonal entries in the iid and some other cases. A similar argument allows one to identify the dominating entries if the time series has a linear dependence structure with regularly varying noise. These techniques are an alternative approach to earlier results by Soshnikov (2004,2006), Auffinger, Ben Arous, Peche (2009), Belinschi, Dembo, Guionnet (2009). They also allow one to deal with certain classes of matrices with dependent heavy-tailed entries. This is joint work with Richard A. Davis (Columbia) and Johannes Heiny
+(Aarhus).
-* Vendredi **16 mars**
+         * 15h30-16h30:  **[[http://umr-math.univ-mlv.fr/membres/tian.peng|Peng Tian]]** //Large Random Matrices of Long Memory Stationary Processes: Asymptotics and fluctuations of the largest eigenvalue \\ //Given $n$ i.i.d. samples $(\boldsymbol{\vec x}_1, \cdots, \boldsymbol{\vec x}_n)$ of a $N$-dimensional long memory stationary process, it has recently been proved that the limiting spectral distribution of the sample covariance matrix, $$\frac 1n \sum_{i=1}^n \boldsymbol{\vec x}_i \boldsymbol{\vec x}^*_i$$ has an unbounded support as $N,n\to \infty$ and $\frac Nn\to c\in (0,\infty)$. As a consequence, its largest eigenvalue  $$\lambda_{\max} \left( \frac 1n \sum_{i=1}^n \boldsymbol{\vec x}_i \boldsymbol{\vec x}^*_i\right)$$  tends to $+\infty$. In this talk, we will describe its asymptotics and fluctuations, tightly related to the features of the underlying population covariance matrix, which is of a Toeplitz nature. This is a joint work with Florence Merlevède and Jamal Najim.
 * Vendredi **6 avril**
+         * 10h30-12h00: mini cours par **[[http://www.proba.jussieu.fr/dw/doku.php?id=users:benhamou:index|Anna Ben Hamou]]**
 * Vendredi **11 mai**
+         * 10h30-12h00: mini cours par **[[http://google.com/search?q=Maxime+Février+Maths|Maxime Février]]**
 * Vendredi **8 juin**
 ===== Année 2016-2017 =====