Optimization theory and related topics : Israel mathematical conference proceedings, a workshop in memory of Dan Butnariu, January 11-14, 2010, Haifa, Israel

Preface.
Biography and Bibliography of Dan Butnariu.
Conference Program.
Sensitivity Estimates Via Lyapunov Functions and Lyapunov Metrics.
1. Introduction.
2. Contraction-like mappings.
3. Lyapunov metrics.
4. Construction of Lyapunov metrics.
5. Equivalence of the metrics.
6. Error estimates for fixed points.
7. Error estimates for trajectories.
References.
On the Maximal Monotonicity of the Sum of a Maximal Monotone Linear Relation and the Subdifferential Operator of a Sublinear Function.
1. Introduction.
2. Auxiliary Results.
3. Main Result.
Acknowledgment.
References.
An Inverse Newton Transform.
1. Introduction.
2. Results.
3. Examples.
4. The logistic iteration.
5. Complex iterations.
References.
Infinite-Horizon Discrete-Time Pontryagin Principles via Results of Michel.
1. Introduction.
2. Conditions and recall.
3. Weak Pontryagin principles.
4. Strong Pontryagin principles.
5. Proof of Theorem 3.1.
6. Proof of Theorem 3.2.
7. Proofs of Theorem 4.1 and Theorem 4.2.
References.
On Sharing of Risk and Resources.
1. Introduction.
2. An Example.
3. Efficient Sharing under Possibly Non-convex Preferences.
4. Risk Sharing.
5. Efficient Sharing as a Differential System.
6. Who should actively share?.
7. Concluding Remarks.
References.
The Expected Retraction Method in Banach Spaces.
1. Introduction.
2. Definitions.
3. The expected retraction operator.
4. The relaxed expected retraction operator.
5. ERM iterations.
References.
Solution of a Singular Optimal Control Problem with State Delays: A Cheap Control Approach.
1. Introduction.
2. Problem statement and main assumptions.
3. Regularization of the OOCP.
4. Asymptotic solution of the CCP.
5. Main results.
References.
Robust Reduction of Dimension of a Linear Programming Problem with Uncertainties: Implication for Robust Production and Technology Planning.
1. Introduction.
2. LP model.
3. Box-constrained uncertainty.
4. Finding redundant rows.
5. Equivalent transformation.
6. Robust upper bounds for dual variables.
7. Lower bound for an objective.
8. Who will get investments?.
9. More lower bounds.
10. More upper bounds.
11. Finding redundant columns.
12. Numerical examples.
13. Conclusion.
References.
Descent Methods for Mixed Variational Inequalities with Non-Smooth Mappings.
1. Introduction.
2. Preliminaries.
3. Properties of gap functions.
4. Stationarity and construction of descent directions.
5. Descent algorithm with exact linesearch.
6. Descent algorithm with inexact linesearch.
7. Modified descent algorithm with inexact linesearch.
8. Combined descent and proximal point methods.
References.