Parallel Optimization

"This book presents a domain that arises where two different branches of science, namely parallel computations and the theory of constrained optimization, intersect with real life problems. This domain, called parallel optimization, has been developing rapidly under the stimulus of progress in computer technology. The book focuses on parallel optimization methods for large-scale constrained optimization problems and structured linear problems. . . . [It] coversa vast portion of parallel optimization, though full coverage of this domain, as the authors admit, goes far beyond the capacity of a single monograph. This book, however, in over 500 pages brings anexcellent and in-depth presentation of all the major aspects of a process which matches theory and methods of optimization with modern computers. The volume can be recommended for graduate students, faculty, and researchers in any of those fields."--Mathematical Reviews"This book presents a domain that arises where two different branches of science, namely parallel computations and the theory of constrained optimization, intersect with real life problems. This domain, called parallel optimization, has been developing rapidly under the stimulus of progress in computer technology. The book focuses on parallel optimization methods for large-scale constrained optimization problems and structured linear problems. . . . [It] coversa vast portion of parallel optimization, though full coverage of this domain, as the authors admit, goes far beyond the capacity of a single monograph. This book, however, in over 500 pages brings anexcellent and in-depth presentation of all the major aspects of a process which matches theory and methods of optimization with modern computers. The volume can be recommended for graduate students, faculty, and researchers in any of those fields."--Mathematical Reviews

• Foreword ; Preface ; Glossary of Symbols ; 1. Introduction ; Part I Theory ; 2. Generalized Distances and Generalized Projections ; 3. Proximal Minimization with D-Functions ; Part II Algorithms ; 4. Penalty Methods, Barrier Methods and Augmented Lagrangians ; 5. Iterative Methods for Convex Feasibility Problems ; 6. Iterative Algorithms for Linearly Constrained Optimization Problems ; 7. Model Decomposition Algorithms ; 8. Decompositions in Interior Point Algorithms ; Part III Applications ; 9. Matrix Estimation Problems ; 10. Image Reconsturction from Projections ; 11. The Inverse Problem in Radiation Therapy Treatment Planning ; 12. Multicommodity Network Flow Problems ; 13. Planning Under Uncertainty ; 14. Decompositions for Parallel Computing ; 15. Numerical Investigations