A.Y. 2018/2019


  • Analisi complessa e funzionale - Functional Analysis and Complex Analysis
    Nicola Arcozzi with Pavel Mozolyako
  • Integrazione gaussiana e meccanica statistica
    Pierluigi Contucci
    15 h
  • Introduzione alla geometria di Cartan e sue applicazioni
    Emanuele Latini – Andrea Santi
  • Ottimizzazione convessa
    Alessandro Lanza
    10 April 14-16
    11 April 14-16
    15 April 14-16
    17 April 14-16
    29 April 14-16
    6  May 14-16
    7  May 9-12
    In this course, we give an overview of convex optimization methods for signal/image processing applications.

    Topics which we would like to cover:

    • Variational methods for Imaging
    • Review on standard tools in convex optimization such as, e.g., (strong) convexity, sub-differentials, gradient descent.
    • Proximal methods for non-smooth optimization (acceleration à la Nesterov)
    • Composite optimization: forward-backward splitting, accelerated forward-backward, FISTA
    • Alternating Direction Method of Multipliers (ADMM) and Majorize-Minimize approach.
    • Numerical implementation and simulations in MATLAB for exemplar imaging problems.

Short cycles of specialistic seminars


  • Metodi variazionali e PDE per l’elaborazione delle immagini
    Luca Calatroni
    10 - 12 a.m. room Seminario I on May 9, 10, 13, 14, 15, 16 and 17;
    2 - 3 p.m room Seminario I on May 15.
    In this course we will present some classical and recent approaches for some problems in image reconstruction (denoising, deblurring, inpainting, shadow-removal…) formulated in terms of appropriate minimisation problems in infinite-dimensional functional spaces. We will further draw connections between these minimisation problems and parabolic Partial Differential Equations (PDEs) based on non-linear diffusion and possibly combined with transport terms.
    For the practical implementation of the models above, we will review standard finite difference stencils discussing their extensions to anisotropic diffusion and diffusion-transport problems. The course will be complemented by some practical MATLAB classes where simple exemplar problems will be solved by means of some reference iterative algorithms.
    • Classical examples of imaging problems (denoising, deblurring, inpainting, segmentation..). Formulation as ill-posed inverse problems. Variational regularisation methods: regularisation term VS data fitting. Statistical interpretation: MAP estimation (2h)
    • Sobolev spaces, standard methods in calculus of variations: a review. Total variation, the space of functions of bounded variations (2h)
    • Second-order parabolic PDEs for image processing: heat equation, mean-curvature flow. Applications to image processing: linear VS non-linear PDEs. Regularisation of non-smoothness: lagged diffusivity. Anisotropic diffusion and diffusion-transport problems. (4h)
    • Finite differences stencils for PDE-based imaging models. (2h)
    • Numerical implementation and simulations in MATLAB for PDE-based models for image reconstruction (deblurring, inpainting, face fusion). (5h)
  • Microlocal methods for dynamical systems
    Maciej Zworski
    June 3, 4, 6 and 7 / 10:00 -13:00
    Scattering resonances replace bound states/eigenvalues for spectral problems in which escape (scattering) to infinity is possible. These states have rates of oscillation and decay and that information is elegantly encoded in considering the corresponding ``eigenvalues" as poles of the meromorphic continuation of Green functions. The most famous ``pure maths" example is given by zeros of the Riemann zeta function which can be interpreted as resonances for scattering on the modular surface. In ``applied maths" they appear anywhere from gravitational waves to MEMS (Micro-Electro-Mechanical Systems).

    The mini course will provide a gentle introduction in the setting of potential scattering in dimension three. Only basic functional analysis will be a prerequisite.

    1. One dimensional scattering: intuition behind outgoing and incoming waves and the definition of scattering resonances.

    2. Analytic Fredholm theory and, as application, meromorphic continuation of Green's function for potentials scattering in dimension three.

    3. Resonance free regions and expansion of waves in terms of resonances.

    4. Counting resonances: upper bounds and existence (and some open problems). Complex valued potentials with no resonances.

    Section 2 of https://math.berkeley.edu/~zworski/revres.pdf (Bull Math Sci '17) will provide a reference with a more detailed presentation in the forthcoming book http://math.mit.edu/~dyatlov/res/ (AMS '19, to appear).


  • The de Branges theory of Hilbert spaces of entire functions and its applications to spectral theory of differential operators
    Anton Baranov
    info and schedule


  • Topics in global analysis
    Gerardo A. Mendoza
    14 h - May 15 - 30, 2019.

Other activities