We develop a general framework for data-driven approximation of input-output maps
between infinitedimensional spaces. The proposed approach is motivated by the recent …

Most existing graph-based clustering methods need a predefined graph and their clustering
performance highly depends on the quality of the graph. Aiming to improve the multiview …

Graduate Texts in Mathematics bridge the gap between passive study and creative
understanding, offering graduate-level introductions to advanced topics in mathematics. The …

This chapter surveys how semidefinite programming can be used for finding good
approximative solutions to hard combinatorial optimization problems. The chapter begins …

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 …

A new methodology for constructing convex optimization models called disciplined convex
programming is introduced. The methodology enforces a set of conventions upon the …

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 …

We present randomized approximation algorithms for the maximum cut (MAX CUT) and
maximum 2-satisfiability (MAX 2SAT) problems that always deliver solutions of expected …

Semidefinite programming (SDP) is a powerful framework from convex optimization that has
striking potential for data science applications. This paper develops a provably correct …

Convex Analysis is the calculus of inequalities while Convex Optimization is its application.
Analysis is inherently the domain of the mathematician while Optimization belongs to the …