Publications related to An optimal first-order solver for the TV-$L_1$ optical flow problem

A Multi-Agent Primal-Dual Strategy for Composite Optimization over Distributed Features

Ali H. Sayed, Sulaiman A S A E Alghunaim, Ming Yan

This work studies multi-agent sharing optimization problems with the objective function being the sum of smooth local functions plus a convex (possibly non-smooth) function coupling all agents. This scenario arises in many machine learning and engineering ...

IEEE2020

Adaptive Gradient Descent without Descent

Konstantin Mishchenko

We present a strikingly simple proof that two rules are sufficient to automate gradient descent: 1) don’t increase the stepsize too fast and 2) don’t overstep the local curvature. No need for functional values, no line search, no information about the func ...

2020

Distributed Design for Decentralized Control using Chordal Decomposition and ADMM

Maryam Kamgarpour

We propose a distributed design method for decentralized control by exploiting the underlying sparsity properties of the problem. Our method is based on chordal decomposition of sparse block matrices and the alternating direction method of multipliers (ADM ...

2020

Finding Second-Order Stationary Points in Constrained Minimization: A Feasible Direction Approach

Shaul Nadav Hallak

This paper introduces a method for computing points satisfying the second-order necessary optimality conditions for nonconvex minimization problems subject to a closed and convex constraint set. The method comprises two independent steps corresponding to t ...

SPRINGER/PLENUM PUBLISHERS2020

New Algorithmic Paradigms for Discrete Problems using Dynamical Systems and Polynomials

Damian Mateusz Straszak

Optimization is a fundamental tool in modern science. Numerous important tasks in biology, economy, physics and computer science can be cast as optimization problems. Consider the example of machine learning: recent advances have shown that even the most s ...

EPFL2018

Proximity Operators of Discrete Information Divergences

Mireille El Gheche, Giovanni Chierchia

While phi-divergences have been extensively studied in convex analysis, their use in optimization problems often remains challenging. In this regard, one of the main shortcomings of existing methods is that the minimization of phi-divergences is usually pe ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2018

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

A Data-driven Approach to Robust Control of Multivariable Systems by Convex Optimization

Alireza Karimi, Christoph Manuel Kammer

The frequency-domain data of a multivariable system in different operating points is used to design a robust controller with respect to the measurement noise and multimodel uncertainty. The controller is fully parametrized in terms of matrix polynomial fun ...

2017

Calculus of variations with differential forms

Bernard Dacorogna, Saugata Bandyopadhyay, Swarnendu Sil

We study integrals of the form integral(Omega) f (d omega), where 1

European Mathematical Soc2015

PURIFY: a new approach to radio-interferometric imaging

Yves Wiaux, Rafael Eduardo Carrillo Rangel, Jason Douglas McEwen

In a recent article series, the authors have promoted convex optimization algorithms for radio-interferometric imaging in the framework of compressed sensing, which leverages sparsity regularization priors for the associated inverse problem and defines a m ...

Wiley-Blackwell2014

An optimal first-order solver for the TV-$L_1$ optical flow problem

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A Multi-Agent Primal-Dual Strategy for Composite Optimization over Distributed Features

Adaptive Gradient Descent without Descent

Distributed Design for Decentralized Control using Chordal Decomposition and ADMM

Finding Second-Order Stationary Points in Constrained Minimization: A Feasible Direction Approach

New Algorithmic Paradigms for Discrete Problems using Dynamical Systems and Polynomials

Proximity Operators of Discrete Information Divergences

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

A Data-driven Approach to Robust Control of Multivariable Systems by Convex Optimization

Calculus of variations with differential forms

PURIFY: a new approach to radio-interferometric imaging

Adaptive Gradient Descent without Descent

Distributed Design for Decentralized Control using Chordal Decomposition and ADMM

New Algorithmic Paradigms for Discrete Problems using Dynamical Systems and Polynomials

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

A Data-driven Approach to Robust Control of Multivariable Systems by Convex Optimization

A Multi-Agent Primal-Dual Strategy for Composite Optimization over Distributed Features

Finding Second-Order Stationary Points in Constrained Minimization: A Feasible Direction Approach

Proximity Operators of Discrete Information Divergences

Calculus of variations with differential forms

PURIFY: a new approach to radio-interferometric imaging