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20062026
most citedScience with the Einstein Telescope: a comparison of different designs

402 citations

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18 papers · 1 filter

math.OC2024

Low Complexity Regularized Phase Retrieval

Jean-Jacques Godeme, Jalal Fadili

In this paper, we study the phase retrieval problem in the situation where the vector to be recovered has an a priori structure that can encoded into a regularization term. This re…

math.OC2024★ 1 cited

An SDE Perspective on Stochastic Inertial Gradient Dynamics with Time-Dependent Viscosity and Geometric Damping

Rodrigo Maulen-Soto, Jalal Fadili, Hedy Attouch +1

Our approach is part of the close link between continuous dissipative dynamical systems and optimization algorithms. We aim to solve convex minimization problems by means of stocha…

math.OC2024

Stable Phase Retrieval with Mirror Descent

Jean-Jacques Godeme, Jalal Fadili, Claude Amra +1

In this paper, we aim to reconstruct an n-dimensional real vector from m phaseless measurements corrupted by an additive noise. We extend the noiseless framework developed in [15],…

math.OC2024

The stochastic Ravine accelerated gradient method with general extrapolation coefficients

Hedy Attouch, Jalal Fadili, Vyacheslav Kungurtsev

In a real Hilbert space domain setting, we study the convergence properties of the stochastic Ravine accelerated gradient method for convex differentiable optimization. We consider…

math.OC2023

Accelerated Gradient Dynamics on Riemannian Manifolds: Faster Rate and Trajectory Convergence

Tejas Natu, Camille Castera, Jalal Fadili +1

In order to minimize a differentiable geodesically convex function, we study a second-order dynamical system on Riemannian manifolds with an asymptotically vanishing damping term o…

math.OC2023★ 6 cited

Continuous Newton-like Methods featuring Inertia and Variable Mass

Camille Castera, Hedy Attouch, Jalal Fadili +1

We introduce a new dynamical system, at the interface between second-order dynamics with inertia and Newton's method. This system extends the class of inertial Newton-like dynamics…