Studying convex matrix functions via their Fenchel conjugates is not a new idea. It is implicit for example in some of the techniquesin [7], and was used ...
Generally the matrix functions involved are spectral: they depend only on the eigenvalues of the matrix. It is known that convex spectral functions can be ...
The convex analysis of unitarily invariant matrix norms is important in matrix approximation. Analogously, the convex analysis of spectral functions of ...
A new approach to convex spectral functions can be characterized exactly as symmetric convex functions of the eigenvalues via a simple Fenchel conjugacy ...
Oct 14, 2012 · If divide through by t and set s=1t it is the generalized eigenvector problem with matrices M=A1−A2 and A2.
There is growing interest in optimization problems with real symmetric matrices as variables. Generally the matrix functions involved are spectral.
Feb 21, 2014 · Suppose we are given a Hermitian matrix A, how to describe the following set of Hermitian S={X:X≥±A}, where Y≥B is Y−B is semidefinite matrix.
Sep 7, 1994 · Generally the matrix functions involved are spectral: they depend only on the eigenvalues of the matrix. It is known that convex spectral ...
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A REMARK ON THE CONVEXITY AND POSITIVE DEFINITENESS CONCERNING HERMITIAN MATRICES. * ... Rockafellar, Convex Analysis, Princeton University Press, Princeton,.
These topics are tied together by their common underlying themes, namely support functions, infimal convolution, and K-convexity. A complete exposition of the.
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