The Euclidean distance degree of orthogonally invariant matrix varieties

The Euclidean distance degree of a real variety is an important invariant arising in distance minimization problems. We show that the Euclidean distance degree of an orthogonally invariant matrix variety equals the Euclidean distance degree of its restriction to diagonal matrices. We illustrate how this result can greatly simplify calculations in concrete circumstances. Article Download … Read more

Variational analysis of spectral functions simplified

Spectral functions of symmetric matrices — those depending on matrices only through their eigenvalues — appear often in optimization. A cornerstone variational analytic tool for studying such functions is a formula relating their subdifferentials to the subdifferentials of their diagonal restrictions. This paper presents a new, short, and revealing derivation of this result. We then … Read more

Approximation Theory of Matrix Rank Minimization and Its Application to Quadratic Equations

Matrix rank minimization problems are gaining a plenty of recent attention in both mathematical and engineering fields. This class of problems, arising in various and across-discipline applications, is known to be NP-hard in general. In this paper, we aim at providing an approximation theory for the rank minimization problem, and prove that a rank minimization … Read more

A characterization of the distance to infeasibility under block-structured perturbations

We discuss several generalizations of the classical Eckart and Young identity. We show that a natural extension of this identity holds for rectangular matrices defining conic systems of constraints, and for perturbations restricted to a particular block structure, such as those determined by a sparsity pattern. Our results extend and unify the classical Eckart and … Read more