Optimal matrices for problems involving the matrix numerical radius often have fields of values that are disks, a phenomenon associated with partial smoothness. Such matrices are highly structured: we experiment in particular with the proximal mapping for the radius, which often maps n-by-n random matrix inputs into a particular manifold of disk matrices that has real codimension 2n. The outputs, computed via semidefinite programming, also satisfy an unusual rank property at optimality.
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ORIE Cornell, April 2020
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View Disk matrices and the proximal mapping for the numerical radius