Publications associées à Joint reconstruction of misaligned images from incomplete measurements for cardiac MRI

Augmented Lagrangian Methods for Provable and Scalable Machine Learning

Non-convex constrained optimization problems have become a powerful framework for modeling a wide range of machine learning problems, with applications in k-means clustering, large- scale semidefinite programs (SDPs), and various other tasks. As the perfor ...

EPFL2023

A first-order primal-dual method with adaptivity to local smoothness

Volkan Cevher, Maria-Luiza Vladarean

We consider the problem of finding a saddle point for the convex-concave objective

\min_x \max_y f(x) + \langle Ax, y\rangle - g^*(y)

, where

f

is a convex function with locally Lipschitz gradient and

g

is convex and possibly non-smooth. We propose an ...

2021

Walkman: A Communication-Efficient Random-Walk Algorithm for Decentralized Optimization

Ali H. Sayed, Kun Yuan

This paper introduces a new algorithm for consensus optimization in a multi-agent network, where all agents collaboratively find a minimizer for the sum of their private functions. All decentralized algorithms rely on communications between adjacent nodes. ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2020

Scalable Semidefinite Programming

Volkan Cevher, Alp Yurtsever

Semidefinite programming (SDP) is a powerful framework from convex optimization that has striking potential for data science applications. This paper develops a provably correct algorithm for solving large SDP problems by economizing on both the storage an ...

2020

Classification with Fairness Constraints: A Meta-Algorithm with Provable Guarantees

Nisheeth Vishnoi, Laura Elisa Celis, Vijay Keswani, Lingxiao Huang

Developing classification algorithms that are fair with respect to sensitive attributes of the data is an important problem due to the increased deployment of classification algorithms in societal contexts. Several recent works have focused on studying cla ...

ASSOC COMPUTING MACHINERY2019

Learning Generalized Nash Equilibria in a Class of Convex Games

Maryam Kamgarpour

We consider multiagent decision making where each agent optimizes its convex cost function subject to individual and coupling constraints. The constraint sets are compact convex subsets of a Euclidean space. To learn Nash equilibria, we propose a novel dis ...

2019

Monogenic Filtering Based Automatic Defect Detection from a Single Fringe Pattern

Pramod Rastogi, Rishikesh Dilip Kulkarni

We propose a fringe analysis algorithm for the automatic detection of defects from a single fringe pattern (FP). Typically, the surface defects exhibit high fringe density areas in the FP. Consequently, high fringe density regions can be utilized as a sign ...

Springer, Cham2019

Active Directional Modifier Adaptation with Trust Region - Application to Energy-Harvesting Kites

Dominique Bonvin, Colin Neil Jones, Matthieu Jonin, Sanket Sanjay Diwale, Martand Singhal

Many real-time optimization schemes maximize process performance by performing a model-based optimization. However, due to plant-model mismatch, the model-based solution is often suboptimal. In modifier adaptation, measurements are used to correct the mode ...

IEEE2018

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

Optical Tomography Based On A Nonlinear Model That Handles Multiple Scattering

Michaël Unser, Demetri Psaltis, Ulugbek Kamilov, Alexandre Goy, Joowon Lim

Learning Tomography (LT) is a nonlinear optimization algorithm for computationally imaging three-dimensional (3D) distribution of the refractive index in semi-transparent samples. Since the energy function in LT is generally non-convex, the solution it obt ...

Ieee2017

Joint reconstruction of misaligned images from incomplete measurements for cardiac MRI

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Augmented Lagrangian Methods for Provable and Scalable Machine Learning

A first-order primal-dual method with adaptivity to local smoothness

Walkman: A Communication-Efficient Random-Walk Algorithm for Decentralized Optimization

Scalable Semidefinite Programming

Classification with Fairness Constraints: A Meta-Algorithm with Provable Guarantees

Learning Generalized Nash Equilibria in a Class of Convex Games

Monogenic Filtering Based Automatic Defect Detection from a Single Fringe Pattern

Active Directional Modifier Adaptation with Trust Region - Application to Energy-Harvesting Kites

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

Optical Tomography Based On A Nonlinear Model That Handles Multiple Scattering

Augmented Lagrangian Methods for Provable and Scalable Machine Learning

Active Directional Modifier Adaptation with Trust Region - Application to Energy-Harvesting Kites

Learning Generalized Nash Equilibria in a Class of Convex Games

Optical Tomography Based On A Nonlinear Model That Handles Multiple Scattering

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

Walkman: A Communication-Efficient Random-Walk Algorithm for Decentralized Optimization

Monogenic Filtering Based Automatic Defect Detection from a Single Fringe Pattern

Classification with Fairness Constraints: A Meta-Algorithm with Provable Guarantees

Scalable Semidefinite Programming

A first-order primal-dual method with adaptivity to local smoothness