Tuesday, 13:45 - 14:10 h, Room: H 2036


Martin Lotz
Conditioning of the convex feasibility problem and sparse recovery

Coauthor: Dennis Amelunxen


The problem of whether certain simple or sparse solutions to linear systems of equations can be found or approximated efficiently can often be cast in terms of a convex feasibility problem. In particular, condition numbers introduced for the complexity analysis of conic optimization problems play an important role in the analysis of such problems. We present results and geometric methods from the probabilistic analysis of condition numbers for optimization problems, and indicate how this analysis can be used to obtain sparse and simple recovery thresholds for problems with noise.


Talk 2 of the invited session Tue.2.H 2036
"Advances in convex optimization" [...]
Cluster 4
"Conic programming" [...]


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