Elements of Classical and Geometric Optimization [Hardcover]

Contents

Chapter 1 Optimization methods  A preview
1.1 Introduction
1.2  The continuous case  mathematical formulation
1.3  The discrete case  The travelling salesman problem
1.4  Basics of probability theory and random number generation      
1.5 The brachistochrone problem
1.6 More on functional optimization: Hamiltons principle
1.7 Constrained optimization problems and optimality conditions
1.8. Functional optimization and optimal control    
Concluding Remarks
Exercises

Notations
References

Chapter 2  Classical derivative-based methods of optimization
2.1 Introduction 
2.2 Basic gradient methods
2.3 Quasi-Newton methods 
2.4 Penalty function methods
 2.5 Linear programming (LP) 
2.6. Method of generalized reduced gradients
2.7 Method of feasible directions 
2.8 Method of gradient projection 
Concluding remarks       
Exercises
Notations
References
 
Chapter 3  Classical derivative-free methods of optimization
3.1 Introduction  
3.2 Direct search methods  
3.3   Other direct search methods  
3.4  Metaheuristics - Evolutionary methods

Concluding remarks  
Exercises
Notations
References

Chapter 4 Elements of Riemannian Differential Geometry and geometric methods of optimization

4.1 Introduction