Abstract. We propose a new family of cost functions for signal and image recovery: they are composed of 1 data fitting terms combined with concave regularization. We exhibit when and how to employ such cost functions. Our theoretical results show that the minimizers of these cost functions

of the teeth from CCD cameras are used to create three dimensional (3-D) CAD model. This approach involves using shape recovery from a series of profile images of teeth. A camera calibration technique is used to minimize the image distortion caused by lenses. Assuming the teeth have no regional concaved

Risk-based capital measures for operational risk management

ii iii CHRISTOPHER CHARLES WEIGLE: Displays for Exploration and Comparison of Nested

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HAL is a multi-disciplinary open access archive for the deposit and dissemination of sci-entific research documents, whether they are pub-lished or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et a ̀ la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ou étrangers, des laboratoires publics ou privés. manuscript No. (will be inserted by the editor) Decomposition algorithm for large-scale two-stage unit-commitment W. van Ackooij · J. Malick Received: date / Accepted: date Abstract Everyday, electricity generation companies submit a generation schedule to the grid operator for the coming day; computing an optimal schedule is called the unit-commitment problem. Generation companies can also occasionally submit changes to the schedule, that can be seen as intra-daily incomplete recourse actions. In this paper, we propose a two-stage formulation of unit-commitment, wherein both the first and second stage problems are full unit-commitment problems. We present a primal-dual decomposition approach to tackle large-scale instances of these two-stage problems. The algorithm makes extensive use of warm-started bundle algorithms, and requires no specific knowledge of the underlying technical constraints. We provide an analysis of the theoretical properties of the algorithm, as well as computational experiments showing the interest of the approach for real-life large-scale unit-commitment instances.

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