Concept

Logarithmically concave function

Publications associées (26)

Gamma-Blind Fast Neutron Detection for Spent Nuclear Fuel Characterization

In this thesis, the development and testing of a system for measuring the axial distribution of fast neutron emission of spent nuclear fuel rods is presented. Emphasis is placed on the novel fast neutron detector used which can reliably work in extremely h ...

EPFL2023

Surface roughness and shear resistance in cracked concrete

Guillaume Anciaux, Mohit Pundir

In concrete structures, opened cracks contribute significantly to the transfer of shear and normal stresses through the contact forces acting on fractured surfaces. Such contact forces are due to protruding asperities, engaged by interlocking and friction ...

2022

Double-Loop Unadjusted Langevin Algorithm

Volkan Cevher, Paul Thierry Yves Rolland, Ali Kavis, Armin Eftekhari

A well-known first-order method for sampling from log-concave probability distributions is the Unadjusted Langevin Algorithm (ULA). This work proposes a new annealing step-size schedule for ULA, which allows to prove new convergence guarantees for sampling ...

2020

Direct FEM-Domain Decomposition Using Convex-to-Concave Spherical Ports for Space Applications

Juan Ramon Mosig, Javier Rubio

This letter proposes the application of a hybrid numerical technique to two cases of interest in the space business: the reallocation of radiators and scatters on satellite platforms; and the analysis of antennas for mobile communications. It is based on t ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2020

On Separable Quadratic Lyapunov Functions for Convex Design of Distributed Controllers

Maryam Kamgarpour, Luca Furieri

We consider the problem of designing a stabilizing and optimal static controller with a pre-specified sparsity pattern. Since this problem is NP-hard in general, it is necessary to resort to approximation approaches. In this paper, we characterize a class ...

IEEE2019

Sampling can be faster than optimization

Nicolas Henri Bernard Flammarion

Optimization algorithms and Monte Carlo sampling algorithms have provided the computational foundations for the rapid growth in applications of statistical machine learning in recent years. There is, however, limited theoretical understanding of the relati ...

PNAS2019

Dimension-free Information Concentration via Exp-Concavity

Volkan Cevher, Ya-Ping Hsieh

Information concentration of probability measures have important implications in learning theory. Recently, it is discovered that the information content of a log-concave distribution concentrates around their differential entropy, albeit with an unpleasan ...

2018

Non-local Functionals Related to the Total Variation and Connections with Image Processing

Hoài-Minh Nguyên

We present new results concerning the approximation of the total variation,

\int_{\Omega} |\nabla u|

, of a function

u

by non-local, non-convex functionals of the form $$ \Lambda_\delta u = \int_{\Omega} \int_{\Omega} \frac{\delta \varphi \big( |u(x) - ...

2018

High-dimensional peaks-over-threshold inference

Anthony Christopher Davison, Raphaël Gérard Théodore Michel Marie de Deloÿe et Fourcade de Fondeville

Max-stable processes are increasingly widely used for modelling complex extreme events, but existing fitting methods are computationally demanding, limiting applications to a few dozen variables. r-Pareto processes are mathematically simpler and have the p ...

2018

Lipschitz changes of variables between perturbations of log-concave measures

Maria Colombo

Extending a result of Caffarelli, we provide global Lipschitz changes of variables between compactly supported perturbations of log-concave measures. The result is based on a combination of ideas from optimal transportation theory and a new Pogorelov-type ...

2017

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Non-local Functionals Related to the Total Variation and Connections with Image Processing

Hoài-Minh Nguyên

We present new results concerning the approximation of the total variation,

\int_{\Omega} |\nabla u|

, of a function

u

by non-local, non-convex functionals of the form $$ \Lambda_\delta u = \int_{\Omega} \int_{\Omega} \frac{\delta \varphi \big( |u(x) - ...

2018