Methods for Solving Regularized Inverse Problems: From Non-Euclidean Fidelities to Computational Imaging Applications
The insertion in June 1997 of a Title on employment in the Treaty on European Union has accelerated the drafting of European policy in this field over the last few years. This European dynamic has had widespread impact on the themes and mechanisms that characterise national systems of industrial relations.
On the one hand, employment is increasingly governed by rules negotiated between the social partners and, depending on the circumstances, the State. This phenomenon of joint labour market regulation is confirmed by a marked desire on the part of employers' associations and trade unions to integrate employment-related issues into their actions and negotiations. On the other hand, the incorporation of employment-related themes by employers’ associations and trade unions, usually in concertation with government policies, is related with greater coordination of bargaining and concertation mechanisms established at European level and within each Member State.
Today, the various national realities appear to be directed to various degrees by these two general tendencies. These phenomena active in the field of employment bargaining must therefore be analysed on three counts: the first focuses on the development of the coordination mechanisms that structure these negotiations, and more specifically raises the issue of co-responsibility for the labour market; the second deals with the strict content of employment bargaining, and examines the question of negotiated flexibility of working conditions and employment; the third addresses the autonomy of collective bargaining in Europe. This analysis informs our research, which is in turn intimately linked to recent changes taking place in national systems of industrial relations.
Pour plus d'informations à propos de la TVA et d'autres moyens de paiement, consultez la rubrique "Paiement & TVA".
Spécifications
- Éditeur
- Presses universitaires de Louvain
- Auteur
- Kévin Degraux,
- Set
- Thèses de l'Université catholique de Louvain (UCL)
- Langue
- anglais
- Site web ressource
- Publisher’s website for a specified work
- BISAC Subject Heading
- TEC000000 TECHNOLOGY & ENGINEERING > TEC009000 TECHNOLOGY & ENGINEERING / Engineering (General)
- Code publique Onix
- 06 Professionnel et académique
- CLIL (Version 2013-2019 )
- 3069 TECHNIQUES ET SCIENCES APPLIQUEES
- Date de première publication du titre
- 2001
Livre broché
- Date de publication
- 19 octobre 2017
- ISBN-13
- 9782875586056
- Illustrations
- 1 bibliography/ 1 glossary glossary
- Ampleur
- Nombre de pages de contenu principal : 226
- Dépôt Légal
- 442 Louvain-la-Neuve, Belgique
- Code interne
- 95771
- Format
- 16 x 24 cm
- Poids
- 367 grammes
- Prix
- 28,80 €
- ONIX XML
- Version 2.1, Version 3
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Sommaire
Nomenclature xxi
1 Introduction 1
1.1 Motivations . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Outline and contributions . . . . . . . . . . . . . . . . . . 4
2 Preliminaries 7
2.1 Regularized inverse problems . . . . . . . . . . . . . . . . 7
2.1.1 Forward model . . . . . . . . . . . . . . . . . . . . 8
2.1.2 Low complexity priors . . . . . . . . . . . . . . . . 15
2.1.3 Sensing model and embedding . . . . . . . . . . . 25
2.2 Recovery methods . . . . . . . . . . . . . . . . . . . . . . 32
2.2.1 General optimization formulation . . . . . . . . . 33
2.2.2 Non-convex recovery methods . . . . . . . . . . . 35
2.2.3 Convex recovery methods . . . . . . . . . . . . . . 41
2.2.4 Algorithms for convex optimization . . . . . . . . 46
2.2.5 Dictionary Learning . . . . . . . . . . . . . . . . . 51
3 Sparse Support Recovery with Convex Fidelity Constraint 57
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . 58
3.1.1 Sparse regularization with convex fidelity constraint 58
3.1.2 Dual Certificates . . . . . . . . . . . . . . . . . . . 60
3.1.3 Main result for sparse support recovery . . . . . . 62
3.1.4 Relation to PriorWorks . . . . . . . . . . . . . . . 64
x Table of contents
3.2 Preliminaries and main result . . . . . . . . . . . . . . . . 65
3.2.1 Noiseless support stability . . . . . . . . . . . . . . 65
3.2.2 Model subspace and restricted injectivity conditions 66
3.2.3 Formal statement of the main result . . . . . . . . 71
3.3 Proofs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74
3.3.1 Proofs of the lemmas and subdifferential decomposability
. . . . . . . . . . . . . . . . . . . . . . . 74
3.3.2 Proof of Theorem 5 . . . . . . . . . . . . . . . . . . 83
3.4 Numerical experiments . . . . . . . . . . . . . . . . . . . . 91
3.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . 94
4 Online Convolutional Dictionary Learning for
Multimodal Imaging 95
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . 96
4.1.1 Main Contributions . . . . . . . . . . . . . . . . . . 97
4.1.2 RelatedWork . . . . . . . . . . . . . . . . . . . . . 99
4.2 Proposed Method . . . . . . . . . . . . . . . . . . . . . . . 100
4.2.1 Problem Formulation . . . . . . . . . . . . . . . . 100
4.2.2 Online Convolutional Dictionary Learning
Algorithm . . . . . . . . . . . . . . . . . . . . . . . 104
4.2.3 Dictionary update . . . . . . . . . . . . . . . . . . 105
4.2.4 Implementation details . . . . . . . . . . . . . . . 107
4.3 Experimental Evaluation . . . . . . . . . . . . . . . . . . . 110
4.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . 117
5 Multispectral Compressive Imaging Strategies using
Fabry-Pérot Filtered Sensors 119
5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . 120
5.1.1 Main Contributions . . . . . . . . . . . . . . . . . . 121
5.1.2 RelatedWork . . . . . . . . . . . . . . . . . . . . . 122
5.2 Preliminaries . . . . . . . . . . . . . . . . . . . . . . . . . . 125
5.2.1 Fabry-Pérot Filtered Sensors . . . . . . . . . . . . 125
5.2.2 Forward model and analysis prior . . . . . . . . . 127
Table of contents xi
5.2.3 Recovery Method . . . . . . . . . . . . . . . . . . . 128
5.3 Multispectral Compressive Imaging by Generalized Inpainting
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132
5.3.1 Image Formation Model . . . . . . . . . . . . . . . 132
5.3.2 Simulations . . . . . . . . . . . . . . . . . . . . . . 135
5.3.3 Experiments . . . . . . . . . . . . . . . . . . . . . . 138
5.4 Multispectral Compressive Imaging by Out-of-Focus
Random Convolution . . . . . . . . . . . . . . . . . . . . . 140
5.4.1 Image Formation Model . . . . . . . . . . . . . . . 140
5.4.2 Non-idealities and practical considerations . . . . 145
5.4.3 Sensing matrix implementation . . . . . . . . . . . 151
5.4.4 Simulations . . . . . . . . . . . . . . . . . . . . . . 153
5.5 Final Comparison . . . . . . . . . . . . . . . . . . . . . . . 155
5.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . 160
6 Conclusions 163
6.1 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . 163
6.2 Perspectives and open questions . . . . . . . . . . . . . . 166
References 173
Appendix A Elements of Convex Optimization 195