Distance metric learning is a branch of machine learning that aims to learn distances from
the data, which enhances the performance of similarity-based algorithms. This tutorial …

This paper discusses characteristic features and inherent difficulties pertaining to the lack of
usual differentiability properties in problems of sensitivity analysis and optimum structural …

We study the optimal design of graph filters (GFs) to implement arbitrary linear
transformations between graph signals. GFs can be represented by matrix polynomials of …

The basic topic of this book is solving problems from system and control theory using convex
optimization. We show that a wide variety of problems arising in system and control theory …

In semidefinite programming, one minimizes a linear function subject to the constraint that
an affine combination of symmetric matrices is positive semidefinite. Such a constraint is …

Variational analysis has been recognized as a fruitful area in mathematics that on the one
hand deals with the study of optimization and equilibrium problems and on the other hand …

This paper addresses the general problem of domain adaptation which arises in a variety of
applications where the distribution of the labeled sample available somewhat differs from …

This work is an attempt to provide a unified presentation of methods for the optimal design of
topology, shape and material for continuum and discrete structures. The emphasis is on …

Modeling realistic fluid and plasma flows is computationally intensive, motivating the use of
reduced-order models for a variety of scientific and engineering tasks. However, it is …

Numerical Linear Algebra and Applications, 2nd Edition: Back Matter Page 1 page 501 i i i i
Bibliography E. Anderson, Z. Bai, C. Bischof, S. Blackford, J. Demmel, J. Dongarra, J. Du …