Projektbeschreibung
Neue Algorithmen behandeln riesige Datenmengen für die Bildverarbeitung
Verschiedene wissenschaftliche Gebiete wie zum Beispiel die Biologie, Medizin, Physik und Chemie produzieren immer größere Datenmengen, die effizient verarbeitet und gespeichert werden müssen. Um diese Datenflut sichten zu können, müssen neue Algorithmen entwickelt werden, die rechnerisch skalierbar, robust und vielseitig einsetzbar sind. Das MM-Framework (Majorization-Minimization), bei dem eine Klasse effizienter und effektiver Optimierungsalgorithmen angewandt wird, spielt bei der effizienten Datenverarbeitung eine entscheidende Rolle. Das EU-finanzierte Projekt MAJORIS wird neue MM-Algorithmen konzipieren, die auch im Umgang mit großen Datenmengen effizient bleiben können. Die Arbeiten werden die Entwicklung von Beschleunigungsstrategien und Konvergenzanalysen beinhalten. Schwerpunktanwendungen sind die hochauflösende Multiphotonenmikroskopie, die Brusttomosynthese-Bildrekonstruktion und die Datenverarbeitung im Bereich der Massenspektrometrie.
Ziel
Mathematical optimization is the key to solving many problems in science, based on the observation that physical systems obey a general principle of least action. While some problems can be solved analytically, many more can only be solved via numerical algorithms. Research in this domain has proved essential over many years. In addition, science in general is changing. Increasingly, in biology, medicine, astronomy, chemistry, physics, large amounts of data are collected by constantly improving signal and image acquisition devices, that must be analyzed by sophisticated optimization tools. In this proposal, we consider handling optimization problems with large datasets. This means minimizing a cost function with a complex structure and many variables. The computational load for solving these problems is too great for even state-of-the-art algorithms. Thus, only relatively rudimentary data processing techniques are employed, reducing the quality of the results and limiting the outcomes that can be achieved via these novel instruments. New algorithms must be designed with computational scalability, robustness and versatility in mind.
In this context, Majorization-Minimization (MM) approaches have a crucial role to play. They consist of a class of efficient and effective optimization algorithms that benefit from solid theoretical foundations. The MAJORIS project aims at proposing a breakthrough in MM algorithms, so that they remain efficient when dealing with big data. I propose to tackle several challenging questions concerning algorithm design. These include acceleration strategies, convergence analysis with complex costs and inexact schemes. I will also tackle practical, massively parallel and distributed architecture implementations. Three specific applications are targeted: super-resolution in multiphoton microscopy in biology; on-the-fly reconstruction for 3D breast tomosynthesis in medical imaging; and mass spectrometry data processing in chemistry.
Wissenschaftliches Gebiet
- natural sciencescomputer and information sciencesdata sciencebig data
- natural sciencesphysical sciencesopticsmicroscopysuper resolution microscopy
- natural sciencesphysical sciencesastronomy
- natural scienceschemical sciencesanalytical chemistrymass spectrometry
- natural sciencescomputer and information sciencesdata sciencedata processing
Programm/Programme
Thema/Themen
Finanzierungsplan
ERC-STG - Starting GrantGastgebende Einrichtung
78153 Le Chesnay Cedex
Frankreich