output
20022026
most citedHigh-Order Synchrosqueezing Transform for Multicomponent Signals Analysis -- With an Application to Gravitational-Wave Signal

518 citations

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Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.

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Showing 2021 · math.OCShow all

7 papers · 2 filters

math.OC2021

Inertial Newton Algorithms Avoiding Strict Saddle Points

Camille Castera

We study the asymptotic behavior of second-order algorithms mixing Newton's method and inertial gradient descent in non-convex landscapes. We show that, despite the Newtonian behav…

math.OC2021

COL0RME: Super-resolution microscopy based on sparse blinking/fluctuating fluorophore localization and intensity estimation

Vasiliki Stergiopoulou, Luca Calatroni, José Henrique de Morais Goulart +2

To overcome the physical barriers caused by light diffraction, super-resolution techniques are often applied in fluorescence microscopy. State-of-the-art approaches require specifi…

math.OC2021

Investment Timing and Technological Breakthroughs

Jean-Paul Décamps, Fabien Gensbittel, Thomas Mariotti

We study the optimal investment policy of a firm facing both technological and cash-flow uncertainty. At any point in time, the firm can decide to invest in a standalone technology…

math.OC2021★ 2 cited

Fast Stochastic Bregman Gradient Methods: Sharp Analysis and Variance Reduction

Radu-Alexandru Dragomir, Mathieu Even, Hadrien Hendrikx

We study the problem of minimizing a relatively-smooth convex function using stochastic Bregman gradient methods. We first prove the convergence of Bregman Stochastic Gradient Desc…

math.OC2021

Hölder Gradient Descent and Adaptive Regularization Methods in Banach Spaces for First-Order Points

Serge Gratton, Sadok Jerad, Philippe L. Toint

This paper considers optimization of smooth nonconvex functionals in smooth infinite dimensional spaces. A Hölder gradient descent algorithm is first proposed for finding approxima…

math.OC2021

Conservative parametric optimality and the ridge method for tame min-max problems

Edouard Pauwels

We study the ridge method for min-max problems, and investigate its convergence without any convexity, differentiability or qualification assumption. The central issue is to determ…

math.OC2021★ 1 cited

A Sublevel Moment-SOS Hierarchy for Polynomial Optimization

Tong Chen, Jean-Bernard Lasserre, Victor Magron +1

We introduce a sublevel Moment-SOS hierarchy where each SDP relaxation can be viewed as an intermediate (or interpolation) between the d-th and (d+1)-th order SDP relaxations of th…