Minimization with orthogonality constraints (eg, X^ ⊤ X= I) and/or spherical constraints (eg,
‖ x ‖ _2= 1) has wide applications in polynomial optimization, combinatorial optimization …

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 many large scale scientific or engineering computations, ranging from computing the
frequency response of a circuit to the earthquake response of a buildingto the energy levels …

The nearest symmetric positive semidefinite matrix in the Frobenius norm to an arbitrary real
matrix A is shown to be (B+ H)/2, where H is the symmetric polar factor of B=(A+ AT)/2. In the …

The Handbook of Linear Algebra provides comprehensive coverage of linear algebra
concepts, applications, and computational software packages in an easy-to-use handbook …

This paper studies the semidefinite programming SDP problem, ie, the optimization problem
of a linear function of a symmetric matrix subject to linear equality constraints and the …

Tight frames, also known as general Welch-bound-equality sequences, generalize
orthonormal systems. Numerous applications-including communications, coding, and …

Inverse eigenvalue problems arise in a remarkable variety of applications and associated
with any inverse eigenvalue problem are two fundamental questions—the theoretical issue …

Polynomial extremal problems (PEP) constitute one of the most important subclasses of
nonlinear programming models. Their distinctive feature is that an objective function and …

A collection of inverse eigenvalue problems are identified and classified according to their
characteristics. Current developments in both the theoretic and the algorithmic aspects are …