Tuesday, 15:45 - 16:10 h, Room: H 2038


Martin Andersen
Multifrontal barrier computations for sparse matrix cones

Coauthor: Vandenberghe Lieven


We discuss conic optimization problems involving two types of convex
matrix cones: the cone of positive semidefinite matrices with a given
chordal sparsity pattern, and its dual cone, the cone of matrices
with the same sparsity that have a positive semidefinite completion.
We describe efficient algorithms for evaluating the values, gradients,
and Hessians of the logarithmic barrier functions for the two types
of cones. The algorithms are based on techniques used in multifrontal
and supernodal sparse Cholesky factorization methods.
The results will be illustrated with applications in covariance selection
and semidefinite programming.


Talk 2 of the invited session Tue.3.H 2038
"Conic and convex programming in statistics and signal processing I" [...]
Cluster 4
"Conic programming" [...]


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