Publications related to Rigorous optimization recipes for sparse and low rank inverse problems with applications in data sciences

A Data-Driven Method for Computing Fixed-Structure Low-Order Controllers With H∞ Performance

Recently, a new data-driven method for robust control with H ∞ performance has been proposed. This method is based on convex optimization and converges to the optimal performance when the controller order increases. However, for low-order controllers, the ...

2018

A Non-Euclidean Gradient Descent Framework for Non-Convex Matrix Factorization

Volkan Cevher, Anastasios Kyrillidis, Ya-Ping Hsieh, Rabeeh Karimi Mahabadi, Yu-Chun Kao

We study convex optimization problems that feature low-rank matrix solutions. In such scenarios, non-convex methods offer significant advantages over convex methods due to their lower space complexity as well as faster convergence speed. Moreover, many of ...

2018

A real-time optimal inverse planning for Gamma Knife radiosurgery by convex optimization: description of the system and first dosimetry data

Jean-Philippe Thiran, Rafael Eduardo Carrillo Rangel, Rémi Charrier

OBJECTIVE The authors developed a new, real-time interactive inverse planning approach, based on a fully convex framework, to be used for Gamma Knife radiosurgery. METHODS The convex framework is based on the precomputation of a dictionary composed of the ...

AMER ASSOC NEUROLOGICAL SURGEONS2018

A Fourier dimensionality reduction model for big data interferometric imaging

Jean-Philippe Thiran, Yves Wiaux, Rafael Eduardo Carrillo Rangel, Vijay Kartik

Data dimensionality reduction in radio interferometry can provide savings of computational resources for image reconstruction through reduced memory footprints and lighter computations per iteration, which is important for the scalability of imaging method ...

Oxford Univ Press2017

AC OPF in radial distribution networks – Part I: On the limits of the branch flow convexification and the alternating direction method of multipliers

Jean-Yves Le Boudec, Mario Paolone, Dan-Cristian Tomozei, Konstantina Christakou

The optimal power-flow problem (OPF) has always played a key role in the planning and operation of power systems. Due to the non-linear nature of the AC power-flow equations, the OPF problem is known to be non-convex, therefore hard to solve. During the la ...

Elsevier2017

Fast Robust PCA on Graphs

Pierre Vandergheynst, Gilles Puy, Nathanaël Perraudin, Nauman Shahid, Vasilis Kalofolias

Mining useful clusters from high dimensional data has received significant attention of the computer vision and pattern recognition community in the recent years. Linear and non-linear dimensionality reduction has played an important role to overcome the c ...

Ieee-Inst Electrical Electronics Engineers Inc2016

Exact Algorithms for $ L ^{ 1 } $ -TV Regularization of Real-Valued or Circle-Valued Signals

Michaël Unser, Martin Kurt Storath

We consider

L ^{ 1 }

-TV regularization of univariate signals with values on the real line or on the unit circle. While the real data space leads to a convex optimization problem, the problem is nonconvex for circle-valued data. In this paper, we deriv ...

SIAM2016

A Comment on “Computational Complexity of Stochastic Programming Problems”

Daniel Kuhn, Grani Adiwena Hanasusanto, Wolfram Wiesemann

Although stochastic programming problems were always believed to be computationally challenging, this perception has only recently received a theoretical justification by the seminal work of Dyer and Stougie (Mathematical Programming A, 106(3):423–432, 200 ...

Springer Heidelberg2016

Modifier Adaptation for Real-Time Optimization -- Methods and Applications

Dominique Bonvin, Grégory François, Timm Faulwasser, Alejandro Gabriel Marchetti

This paper presents an overview of the recent developments of modifier-adaptationschemes for real-time optimization of uncertain processes. These schemes have the ability to reach plant optimality upon convergence despite the presence of structural plant-m ...

MDPI2016

Parallelizing Query Optimization on Shared-Nothing Architectures

Christoph Koch, Immanuel Trummer

Data processing systems offer an ever increasing degree of parallelism on the levels of cores, CPUs, and processing nodes. Query optimization must exploit high degrees of parallelism in order not to gradually become the bottleneck of query evaluation. We s ...

Assoc Computing Machinery2016

Rigorous optimization recipes for sparse and low rank inverse problems with applications in data sciences

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A Data-Driven Method for Computing Fixed-Structure Low-Order Controllers With H∞ Performance

A Non-Euclidean Gradient Descent Framework for Non-Convex Matrix Factorization

A real-time optimal inverse planning for Gamma Knife radiosurgery by convex optimization: description of the system and first dosimetry data

A Fourier dimensionality reduction model for big data interferometric imaging

AC OPF in radial distribution networks – Part I: On the limits of the branch flow convexification and the alternating direction method of multipliers

Fast Robust PCA on Graphs

Exact Algorithms for $ L ^{ 1 } $ -TV Regularization of Real-Valued or Circle-Valued Signals

A Comment on “Computational Complexity of Stochastic Programming Problems”

Modifier Adaptation for Real-Time Optimization -- Methods and Applications

Parallelizing Query Optimization on Shared-Nothing Architectures

AC OPF in radial distribution networks – Part I: On the limits of the branch flow convexification and the alternating direction method of multipliers

A Fourier dimensionality reduction model for big data interferometric imaging

A Non-Euclidean Gradient Descent Framework for Non-Convex Matrix Factorization

A real-time optimal inverse planning for Gamma Knife radiosurgery by convex optimization: description of the system and first dosimetry data

Fast Robust PCA on Graphs

Exact Algorithms for $ L ^{ 1 } $ -TV Regularization of Real-Valued or Circle-Valued Signals

A Comment on “Computational Complexity of Stochastic Programming Problems”

Parallelizing Query Optimization on Shared-Nothing Architectures

Modifier Adaptation for Real-Time Optimization -- Methods and Applications

A Data-Driven Method for Computing Fixed-Structure Low-Order Controllers With H∞ Performance