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Distributed Design for Decentralized Control using Chordal Decomposition and ADMM

Publications associées (85)

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Multi-Objective Airport Gate Assignment Problem in Planning and Operations

Michel Bierlaire, Prem Kumar

We consider the assignment of gates to arriving and departing flights at a large hub airport. It is considered to be a highly complex problem even in planning stage when all flight arrivals and departures are assumed to be known precisely in advance. There ...

2012

Piercing quasi-rectangles-On a problem of Danzer and Rogers

János Pach, Gábor Tardos

It is an old problem of Danzer and Rogers to decide whether it is possible to arrange 0(1/epsilon) points in the unit square so that every rectangle of area epsilon > 0 within the unit square contains at least one of them. We show that the answer to this q ...

Elsevier2012

Functional Webs for Freeform Architecture

Bailin Deng

Rationalization and construction-aware design dominate the issue of realizability of freeform architecture. The former means the decomposition of an intended shape into parts which are sufficiently simple and efficient to manufacture; the latter refers to ...

Wiley-Blackwell2011

Spread Spectrum for Universal Compressive Sampling

Pierre Vandergheynst, Yves Wiaux, Rémi Gribonval, Gilles Puy

We propose a universal and efficient compressive sampling strategy based on the use of a spread spectrum technique. The method essentially consists in a random pre-modulation of the signal of interest followed by projections onto randomly selected vectors ...

2011

Fill-ins number reducing direct solver designed for FIT-type matrix

Michal Karol Dobrzynski, Jagoda Anna Dobrzynska

This paper investigates the particular problem of matrices appearing during the modeling of Integrated Circuits with Finite Integration Technique (FIT) method. We present the key points of FIT approach followed by an illustration of the structure and the p ...

Elsevier2010

Optimal Linear Fusion for Distributed Detection Via Semidefinite Programming

Ali H. Sayed

Consider the problem of signal detection via multiple distributed noisy sensors. We study a linear decision fusion rule of [Z. Quan, S. Cui, and A. H. Sayed, ¿Optimal Linear Cooperation for Spectrum Sensing in Cognitive Radio Networks,¿ IEEE J. Sel. Topics ...

IEEE2010

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

Pierre Vandergheynst, Gilles Puy, Emmanuel D'Angelo

In this study, we address the problem of computing efficiently a dense optical flow between two images under a total variation (TV) regularization and an

L_1

norm data fidelity constraint using a variational method. We build upon Nesterov's framework for ...

2010

Convex polygons are cover-decomposable

We show that for any open convex polygon P, there is a constant k(P) such that any k(P)-fold covering of the plane with translates of P can be decomposed into two coverings. ...

2010

Fast hard thresholding with Nesterov's gradient method

Volkan Cevher, Sina Jafarpour

We provide an algorithmic framework for structured sparse recovery which unifies combinatorial optimization with the non-smooth convex optimization framework by Nesterov [1, 2]. Our algorithm, dubbed Nesterov iterative hard-thresholding (NIHT), is similar ...

2010

Dictionary Identification - Sparse Matrix-Factorisation via $\ell_1$-Minimisation

Rémi Gribonval, Karin Schnass

This article treats the problem of learning a dictionary providing sparse representations for a given signal class, via

\ell_1

-minimisation. The problem can also be seen as factorising a

\ddim \times \nsig

matrix $Y=(y_1 \ldots y_\nsig), , y_n\in \R^\ ...

Institute of Electrical and Electronics Engineers2010

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

Pierre Vandergheynst, Gilles Puy, Emmanuel D'Angelo

In this study, we address the problem of computing efficiently a dense optical flow between two images under a total variation (TV) regularization and an

L_1

norm data fidelity constraint using a variational method. We build upon Nesterov's framework for ...

2010

Dictionary Identification - Sparse Matrix-Factorisation via $\ell_1$-Minimisation

Rémi Gribonval, Karin Schnass

This article treats the problem of learning a dictionary providing sparse representations for a given signal class, via

\ell_1

-minimisation. The problem can also be seen as factorising a

\ddim \times \nsig

matrix $Y=(y_1 \ldots y_\nsig), , y_n\in \R^\ ...

Institute of Electrical and Electronics Engineers2010