Springer eBooks may be purchased by end-customers only and are sold without copy protection (DRM free). Instead, all eBooks include personalized watermarks. This means you can read the Springer eBooks across numerous devices such as Laptops, eReaders, and tablets.
You can pay for Springer eBooks with Visa, Mastercard, American Express or Paypal.
After the purchase you can directly download the eBook file or read it online in our Springer eBook Reader. Furthermore your eBook will be stored in your MySpringer account. So you can always re-download your eBooks.
Starts with illustrations of the ubiquitous character of optimization
Covers fundamental algorithms as well as more specialized and advanced topics
The non-smooth optimization part has been substantially reorganized and expanded
Starting with illustrative real-world examples, this book exposes in a tutorial way algorithms for numerical optimization: fundamental ones (Newtonian methods, line-searches, trust-region, sequential quadratic programming, etc.), as well as more specialized and advanced ones (nonsmooth optimization, decomposition techniques, and interior-point methods). Most of these algorithms are explained in a detailed manner, allowing straightforward implementation. Theoretical aspects are addressed with care, often using minimal assumptions.
The present version contains substantial changes with respect to the first edition. Part I on unconstrained optimization has been completed with a section on quadratic programming. Part II on nonsmooth optimization has been thoroughly reorganized and expanded. In addition, nontrivial application problems have been inserted, in the form of computational exercises. These should help the reader to get a better understanding of optimization methods beyond their abstract description, by addressing important features to be taken into account when passing to implementation of any numerical algorithm.
This level of detail is intended to familiarize the reader with some of the crucial questions of numerical optimization: how algorithms operate, why they converge, difficulties that may be encountered and their possible remedies.
Preliminaries 1 General Introduction
1.1 Generalities on Optimization
1.2 Motivation and Examples
1.3 General Principles of Resolution
1.4 General Convergence Theorem: Zangwill
1.5 Generalities on Convergence
1.6 Computing the Gradient Part I - Unconstrained Problems 2 Basic Methods
2.1 Existence Questions
2.2 Optimality Conditions
2.3 First-Order Methods
2.4 Link with the General Descent Scheme
2.5 Steepest-Descent Method
2.6 Implementation 3 Line-Searches
3.1 General Scheme
3.2 Computing the New t
3.3 Optimal Stepsize (for the record only)
3.4 Modern Line-Search: Wolfe's Rule
3.5 Other Line-Searches: Goldstein and Price, Armijo
3.6 Implementation Considerations 4 Newtonian Methods
4.1 Preliminaries
4.2 Forcing Global Convergence
4.3 Alleviating the Method
4.4 Quasi-Newton Methods
4.5 Global Convergence
4.6 Local Convergence: Generalities
4.7 Local Convergence: BFGS 5 Conjugate Gradient
5.1 Outline of Conjugate Gradient
5.2 Developing the Method
5.3 Computing the Direction
5.4 The Algorithm Seen as an Orthogonalization Process
5.5 Application to Non-Quadratic Functions
5.6 Relation with Quasi-Newton 6 Special Methods
6.1 Trust-Regions
6.2 Least-Squares Problems: Gauss-Newton
6.3 Large-Scale Problems: Limited-Memory Quasi-Newton
6.4 Truncated Newton Part II - Nonsmooth Optimization 7 Some Theory of Nonsmooth Optimization
7.1 First Elements of Convex Analysis
7.2 Lagrangian Relaxation and Duality
7.3 Two Convex Nondifferentiable Functions 8 Some Methods in Nonsmooth Optimization
8.1 Why Special Methods?
8.2 Descent Methods
8.3 Two Black-Box Methods 9 Bundle Methods. The Quest of Descent
9.1 Stabilization. A Primal Approach
9.2 Some Examples of Stabilized Problems
9.3 Penalized Bundle Methods 10 Decomposition and Duality
10.1 Primal-DualRelations
10.2 Back to the Primal. Recovering Primal Solutions
10.3 Price Decomposition
10.4 Resource Decomposition
10.5 Other Decomposition Techniques Part III - Newton's Methods in Constrained Optimization 11 Background
11.1 Differential Calculus
11.2 Existence and Uniqueness of Solutions
11.3 First-Order Optimality Conditions
11.4 Second-Order Optimality Conditions
11.5 Speed of Convergence
11.6 Projection onto a Closed Convex Set
11.7 The Newton Method
Exercises 12 Local Methods for Problems with Equality Constraints
12.1 Newton's Method
12.2 Adapted Decompositions of {\@mathbb R}^n
12.3 Local Analysis of Newton's Method
12.4 Computation of the Newton Step
12.5 Reduced Hessian Algorithm
12.6 A Comparison of the Algorithms
Notes
Exercises 13 Local Methods for Problems with Equality and Inequality Constraints
13.1 The SQP Algorithm
13.2 Primal-Dual Quadratic Convergence
13.3 Primal Superlinear Convergence
Notes 14 Exact Penalization
14.1 Overview
14.2 The Lagrangian
14.3 The Augmented Lagrangian
14.4 Nondifferentiable Augmented Function
Notes
Exercises 15 Globalization by Line-Search
15.1 Line-Search SQP Algorithms
15.2 Truncated SQP
15.3 From Global to Local
Notes
Exercises 16 Quasi-Newton Versions
16.1 Principles
16.2 Quasi-Newton SQP
16.3 Reduced Quasi-Newton Algorithm Part IV - Interior-Point Algorithms for Linear and Quadratic Optimization 17 Linearly Constrained Optimization and Simplex Algorithm
17.1 Existence of Solutions
17.2 Duality
17.3 The Simplex Algorithm
17.4 Comments 18 Linear Monotone Complementarity and Associated Vector Fields
18.1 Logarithmic Penalty and Central Path
18.2 Linear Monotone Complementarity
18.3 Vector Fields Associated with the Central Path
18.4 Continuous Trajectories
18.5 Comments 19 Predictor-Corrector