Parallel Optimization: Theory, Algorithms, and Applications

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