Excerpt More than 25 years have passed since the first edition of this book was published in
1996. Least squares and least-norm problems have become more significant with every …

Interior point methods for optimization have been around for more than 25 years now. Their
presence has shaken up the field of optimization. Interior point methods for linear and …

Significant increases in processing power, coupled with the miniaturization of processing
units operating at low power levels, has motivated the embedding of modern control systems …

Cable-driven parallel robots (CDPRs) use cables instead of the rigid limbs of traditional
parallel robots, thus processing a large potential workspace, easy to assemble and …

This paper introduces QPDO, a primal-dual method for convex quadratic programs which
builds upon and weaves together the proximal point algorithm and a damped semismooth …

Large-scale optimization problems that seek sparse solutions have become ubiquitous.
They are routinely solved with various specialized first-order methods. Although such …

In this paper we combine an infeasible Interior Point Method (IPM) with the Proximal Method
of Multipliers (PMM). The resulting algorithm (IP-PMM) is interpreted as a primal-dual …

Convex quadratic programming (QP) is an important class of optimization problem with wide
applications in practice. The classic QP solvers are based on either simplex or barrier …

Interior-point methods feature prominently among numerical methods for inequality-
constrained optimization problems, and involve the need to solve a sequence of linear …

As the zeroing neural network (ZNN) approach needs human intervention to handle
temporally variant quadratic program (TVQP) problem, which results in less flexibility and …