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

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This paper proposes a smoothing technique for nonsmooth convex minimization using self-concordant barriers. To illustrate the main ideas, we compare our technique and the proximity smoothing approach (Nesterov2005) via the classical gradient method on both ...

Ieee2014

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

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The convex

\ell_1

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

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Computational Complexity Certification for Real-Time MPC With Input Constraints Based on the Fast Gradient Method

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This paper proposes to use Nesterov's fast gradient method for the solution of linear quadratic model predictive control (MPC) problems with input constraints. The main focus is on the method's a priori computational complexity certification which consists ...

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Towards computational complexity certification for constrained mpc based on lagrange relaxation and the fast gradient method

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Solving a convex optimization problem within an a priori certified period of time is a challenging problem. This paper discusses the certification of Nesterov’s fast gradient method for problems with a strictly quadratic objective and a feasible set given ...

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A Primal-Dual Reconstruction Algorithm For Fluorescence And Bioluminescence Tomography

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Ieee Service Center, 445 Hoes Lane, Po Box 1331, Piscataway, Nj 08855-1331 Usa2011

A Primal-Dual Reconstruction Algorithm for Fluorescence and Bioluminescence Tomography

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We introduce a new primal-dual reconstruction algorithm for fluorescence and bioluminescence tomography. As often in optical tomography, image reconstruction is performed by optimizing a multi-term convex cost function. Current reconstruction methods emplo ...

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Optimization Problem Coupled with Differential Equations: A Numerical Algorithm Mixing an Interior-Point Method and Event Detection

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The numerical analysis of a dynamic constrained optimization problem is presented. It consists of a global minimization problem that is coupled with a system of ordinary differential equations. The activation and the deactivation of inequality constraints ...

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Numerical analysis of optimization-constrained differential equations

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The modeling of a system composed by a gas phase and organic aerosol particles, and its numerical resolution are studied. The gas-aerosol system is modeled by ordinary differential equations coupled with a mixed-constrained optimization problem. This coupl ...

EPFL2009

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

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Barrier Smoothing for Nonsmooth Convex Minimization

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

Process Design and Optimization (MLS-S03): Model Identification by Gradient Methods

Computational Complexity Certification for Real-Time MPC With Input Constraints Based on the Fast Gradient Method

Towards computational complexity certification for constrained mpc based on lagrange relaxation and the fast gradient method

A Primal-Dual Reconstruction Algorithm For Fluorescence And Bioluminescence Tomography

A Primal-Dual Reconstruction Algorithm for Fluorescence and Bioluminescence Tomography

Optimization Problem Coupled with Differential Equations: A Numerical Algorithm Mixing an Interior-Point Method and Event Detection

Numerical analysis of optimization-constrained differential equations

Computational Complexity Certification for Real-Time MPC With Input Constraints Based on the Fast Gradient Method

A Primal-Dual Reconstruction Algorithm For Fluorescence And Bioluminescence Tomography

Barrier Smoothing for Nonsmooth Convex Minimization

Towards computational complexity certification for constrained mpc based on lagrange relaxation and the fast gradient method

Numerical analysis of optimization-constrained differential equations

Process Design and Optimization (MLS-S03): Model Identification by Gradient Methods

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

A Primal-Dual Reconstruction Algorithm for Fluorescence and Bioluminescence Tomography

Optimization Problem Coupled with Differential Equations: A Numerical Algorithm Mixing an Interior-Point Method and Event Detection