https://popups.uliege.be/3041-539x/index.php?id=2198 Index terms fr 0 https://popups.uliege.be/3041-539x/index.php?id=2195 Optimization theory is a key technology for inverse problems of reconstruction with applications in science, technology and economy. Discrete tomography is a modern research field which deals with finite objects from VLSI chip design or medical imaging. This paper focuses on the utilization of modern optimization methods to approximately resolve the NP-hard reconstruction problem of discrete tomography. Our new approaches and introductions are based on modeling and algorithms from coding theory and optimal experimental design. Here, we combine continuous and discrete optimization with exploiting geometrical symmetries, or more generally, equivariances, in a framework of statistical learning. Tue, 30 Jul 2024 13:22:50 +0200 Tue, 30 Jul 2024 13:23:02 +0200 https://popups.uliege.be/3041-539x/index.php?id=2195