Publications associées à Decomposition Strategies for Nonconvex Problems, a Parametric Approach

An optimal first-order primal-dual gap reduction framework for constrained convex optimization

We introduce an analysis framework for constructing optimal first-order primal-dual methods for the prototypical constrained convex optimization template. While this class of methods offers scalability advantages in obtaining numerical solutions, they have ...

Tech. Report. LIONS-EPFL (2015)2015

Analysis of the finite element heterogeneous multiscale method for quasilinear elliptic homogenization problems

Assyr Abdulle, Gilles Vilmart

A fully discrete analysis of the finite element heterogeneous multiscale method for a class of nonlinear elliptic homogenization problems of nonmonotone type is proposed. In contrast to previous results obtained for such problems in dimension

d\leq2

for ...

Amer Mathematical Soc2014

Certification of fixed computation time first-order optimization-based controllers for a class of nonlinear dynamical systems

Colin Neil Jones, Milan Korda

This paper proposes a stability verification method for systems controlled by an early terminated first-order method (e.g., an MPC problem approximately solved by a fixed number of iterations of the fast gradient method). The method is based on the observa ...

Ieee2014

Path-following gradient-based decomposition algorithms for separable convex optimization

Quoc Tran Dinh

A new decomposition optimization algorithm, called path-following gradient-based decomposition, is proposed to solve separable convex optimization problems. Unlike path-following Newton methods considered in the literature, this algorithm does not require ...

Springer Verlag2014

Approximating dynamic equilibrium conditions with macroscopic fundamental diagrams

Nikolaos Geroliminis, Mehmet Yildirimoglu

Real-time coordinated traffic management strategies that benefit from parsimonious models with aggregated network dynamics, provide a new generation of smart hierarchical strategies to improve network capacity and performance. However, this raises the ques ...

Elsevier2014

Use of Convex Model Approximations for Real-Time Optimization via Modifier Adaptation

Dominique Bonvin, Grégory François

Real-Time Optimization (RTO) via modifier adaptation is a class of methods for which measurements are used to iteratively adapt the model via input-affine additive terms. The modifier terms correspond to the deviations between the measured and predicted co ...

American Chemical Society2013

A proximal Newton framework for composite minimization: Graph learning without Cholesky decompositions and matrix inversions

Volkan Cevher, Quoc Tran Dinh, Anastasios Kyrillidis

We propose an algorithmic framework for convex minimization problems of a composite function with two terms: a self-concordant function and a possibly nonsmooth regularization term. Our method is a new proximal Newton algorithm that features a local quadra ...

2013

Fast Proximal algorithms for Self-concordant function minimization with application to sparse graph selection

Volkan Cevher, Anastasios Kyrillidis

The convex

\ell_1

-regularized

\log\det

divergence criterion has been shown to produce theoretically consistent graph learning. However, this objective function is challenging since the

\ell_1

-regularization is nonsmooth, the

\log\det

objective is n ...

2013

Fast Proximal Algorithms For Self-Concordant Function Minimization With Application To Sparse Graph Selection

Volkan Cevher, Anastasios Kyrillidis

The convex l(1)-regularized log det divergence criterion has been shown to produce theoretically consistent graph learning. However, this objective function is challenging since the l(1)-regularization is nonsmooth, the log det objective is not globally Li ...

IEEE2013

Accelerated And Inexact Forward-Backward Algorithms

Luca Baldassarre

We propose a convergence analysis of accelerated forward-backward splitting methods for composite function minimization, when the proximity operator is not available in closed form, and can only be computed up to a certain precision. We prove that the 1/k( ...

Siam Publications2013

Decomposition Strategies for Nonconvex Problems, a Parametric Approach

Graph Chatbot

Chattez avec Graph Search

An optimal first-order primal-dual gap reduction framework for constrained convex optimization

Analysis of the finite element heterogeneous multiscale method for quasilinear elliptic homogenization problems

Certification of fixed computation time first-order optimization-based controllers for a class of nonlinear dynamical systems

Path-following gradient-based decomposition algorithms for separable convex optimization

Approximating dynamic equilibrium conditions with macroscopic fundamental diagrams

Use of Convex Model Approximations for Real-Time Optimization via Modifier Adaptation

A proximal Newton framework for composite minimization: Graph learning without Cholesky decompositions and matrix inversions

Fast Proximal algorithms for Self-concordant function minimization with application to sparse graph selection

Fast Proximal Algorithms For Self-Concordant Function Minimization With Application To Sparse Graph Selection

Accelerated And Inexact Forward-Backward Algorithms

Approximating dynamic equilibrium conditions with macroscopic fundamental diagrams

Analysis of the finite element heterogeneous multiscale method for quasilinear elliptic homogenization problems

Certification of fixed computation time first-order optimization-based controllers for a class of nonlinear dynamical systems

Fast Proximal Algorithms For Self-Concordant Function Minimization With Application To Sparse Graph Selection

Use of Convex Model Approximations for Real-Time Optimization via Modifier Adaptation

Path-following gradient-based decomposition algorithms for separable convex optimization

Fast Proximal algorithms for Self-concordant function minimization with application to sparse graph selection

A proximal Newton framework for composite minimization: Graph learning without Cholesky decompositions and matrix inversions

Accelerated And Inexact Forward-Backward Algorithms

An optimal first-order primal-dual gap reduction framework for constrained convex optimization