J. Ben Rosen

Constrained global optimization: algorithms and applications

Convex sets and functions.- Optimality conditions in nonlinear programming.- Combinatorial optimization problems that can be formulated as nonconvex quadratic problems.- Enumerative methods in nonconvex programming.- Cutting plane methods.- Branch and bound methods.- Bilinear programming methods for nonconvex quadratic problems.- Large scale problems.- Global minimization of indefinite quadratic problems.- Test problems for global nonconvex quadratic programming algorithms.

Total Least Norm Formulation and Solution for Structured Problems

A new formulation and algorithm is described for computing the solution to an overdetermined linear system,

Lower Dimensional Representation of Text Data Based on Centroids and Least Squares

Ax \approx b

An analytical approach to floorplan design and optimization

, with possible errors in both

Computational comparison of two algorithms for the Euclidean single facility location problem

A

Iterative Convex Quadratic Approximation for Global Optimization in Protein Docking

and

Coupled optimization in protein docking

b

Accurate Solution to Overdetermined Linear Equations with Errors Using L1 Norm Minimization

. This approach preserves any affine structure of

Convex Quadratic Approximation

A

Fast data transmission and maximal dynamic flow

or