Deterministic Global Optimization: Theory, Methods and Applications

Preface

1. Introduction

2. Basic Concepts of Global Optimization

Part I: Biconvex and Bilinear Problems

3. The GOP Primal-Relaxed Dual Decomposition Approach: Theory

4. The GOP Approach: Implementation and Computational Studies

5. The GOP Approach in Bilevel Linear and Quadratic Problems

6. The GOP Approach in Phase and Chemical Equilibrium Problems

7. The GOP Approach: Distributed Implementation

Part II: Signomial Problems

8. Generalized Geometric Programming: Theory

9. Generalized Geometric Programming: Computational Studies

Part III: Towards General Twice Differentiable NLPs

10. From Biconvex to General Twice Differentiable NLPs

11. The alphaBB For Box Constrained Twice-Differentiable NLPs: Theory

12. The alphaBB for Constrained Twice-Differentiable NLPs: Theory

13. Computational Studies of the alphaBB Approach

14. Global Optimization in Microclusters

15. The alphaBB Approach in Molecular Structure Prediction

16. The alphaBB Approach in Protein Folding

17. The alphaBB Approach in Peptide Docking

18. The alphaBB Approach in Batch Design Under Uncertainty

19. The alphaBB Approach in Parameter Estimation

Part IV: Nonlinear and Mixed-Integer Optimization

20. Introduction to Nonlinear and Mixed-Integer Optimization

21. The SMIN-alphaBB Approach: Theory and Computations

22. The GMIN-alphaBB Approach: Theory and Computations

Part V: Nonlinear Constrained Systems of Equations

23. All Solutions of Nonlinear Constrained Systems of Equations

24. Locating all Homogeneous Azeotropes

- References

- Indextrained Twice-Differentiable NLPs: Theory

13. Comp