Title Page -- Contents -- Foreword -- Preface -- List of Acronyms -- Chapter 1. Linear Programming -- 1.1. Objective of linear programming -- 1.2. Stating the problem -- 1.3. Lagrange method -- 1.4. Simplex algorithm -- 1.4.1. Principle -- 1.4.2. Simplicial form formulation -- 1.4.3. Transition from one simplicial form to another -- 1.4.4. Summary of the simplex algorithm -- 1.5. Implementation example -- 1.6. Linear programming applied to the optimization of resource allocation -- 1.6.1. Areas of application -- 1.6.2. Resource allocation for advertising -- 1.6.3. Optimization of a cut of paper rolls -- 1.6.4. Structure of linear program of an optimal control problem -- Chapter 2. Nonlinear Programming -- 2.1. Problem formulation -- 2.2. Karush-Kuhn-Tucker conditions -- 2.3. General search algorithm -- 2.3.1. Main steps -- 2.3.2. Computing the search direction -- 2.3.3. Computation of advancement step -- 2.4. Monovariable methods -- 2.4.1. Coggin's method (of polynomial interpolation) -- 2.4.2. Golden section method -- 2.5. Multivariable methods -- 2.5.1. Direct search methods -- 2.5.2. Gradient methods -- Chapter 3. Dynamic Programming -- 3.1. Principle of dynamic programming -- 3.1.1. Stating the problem -- 3.1.2. Decision problem -- 3.2. Recurrence equation of optimality -- 3.3. Particular cases -- 3.3.1. Infinite horizon stationary problems -- 3.3.2. Variable horizon problem -- 3.3.3. Random horizon problem -- 3.3.4. Taking into account sum-like constraints -- 3.3.5. Random evolution law -- 3.3.6. Initialization when the final state is imposed -- 3.3.7. The case when the necessary information is not always available -- 3.4. Examples -- 3.4.1. Route optimization -- 3.4.2. The smuggler problem -- Chapter 4. Hopfield Networks -- 4.1. Structure -- 4.2. Continuous dynamic Hopfield networks -- 4.2.1. General problem.

4.2.2. Application to the traveling salesman problem -- 4.3. Optimization by Hopfield networks, based on simulated annealing -- 4.3.1. Deterministic method -- 4.3.2. Stochastic method -- Chapter 5. Optimization in System Identification -- 5.1. The optimal identification principle -- 5.2. Formulation of optimal identification problems -- 5.2.1. General problem -- 5.2.2. Formulation based on optimization theory -- 5.2.3. Formulation based on estimation theory (statistics) -- 5.3. Usual identification models -- 5.3.1. General model -- 5.3.2. Rational input/output (RIO) models -- 5.3.3. Class of autoregressive models (ARMAX) -- 5.3.4. Class of state space representation models -- 5.4. Basic least squares method -- 5.4.1. LSM type solution -- 5.4.2. Geometric interpretation of the LSM solution -- 5.4.3. Consistency of the LSM type solution -- 5.4.4. Example of application of the LSM for an ARX model -- 5.5. Modified least squares methods -- 5.5.1. Recovering lost consistency -- 5.5.2. Extended LSM -- 5.5.3. Instrumental variables method -- 5.6. Minimum prediction error method -- 5.6.1. Basic principle and algorithm -- 5.6.2. Implementation of the MPEM for ARMAX models -- 5.6.3. Convergence and consistency of MPEM type estimations -- 5.7. Adaptive optimal identification methods -- 5.7.1. Accuracy/adaptability paradigm -- 5.7.2. Basic adaptive version of the LSM -- 5.7.3. Basic adaptive version of the IVM -- 5.7.4. Adaptive window versions of the LSM and IVM -- Chapter 6. Optimization of Dynamic Systems -- 6.1. Variational methods -- 6.1.1. Variation of a functional -- 6.1.2. Constraint-free minimization -- 6.1.3. Hamilton canonical equations -- 6.1.4. Second-order conditions -- 6.1.5. Minimization with constraints -- 6.2. Application to the optimal command of a continuous process, maximum principle -- 6.2.1. Formulation -- 6.2.2. Examples of implementation.

6.3. Maximum principle, discrete case -- 6.4. Principle of optimal command based on quadratic criteria -- 6.5. Design of the LQ command -- 6.5.1. Finite horizon LQ command -- 6.5.2. The infinite horizon QL command -- 6.5.3. Robustness of the LQ command -- 6.6. Optimal filtering -- 6.6.1. Kalman-Bucy predictor -- 6.6.2. Kalman-Bucy filter -- 6.6.3. Stability of Kalman-Bucy estimators -- 6.6.4. Robustness of Kalman-Bucy estimators -- 6.7. Design of the LQG command -- 6.8. Optimization problems connected to quadratic linear criteria -- 6.8.1. Optimal control by state feedback -- 6.8.2. Quadratic stabilization -- 6.8.3. Optimal command based on output feedback -- Chapter 7. Optimization of Large-Scale Systems -- 7.1. Characteristics of complex optimization problems -- 7.2. Decomposition techniques -- 7.2.1. Problems with block-diagonal structure -- 7.2.2. Problems with separable criteria and constraints -- 7.3. Penalization techniques -- 7.3.1. External penalization technique -- 7.3.2. Internal penalization technique -- 7.3.3. Extended penalization technique -- Chapter 8. Optimization and Information Systems -- 8.1. Introduction -- 8.2. Factors influencing the construction of IT systems -- 8.3. Approaches -- 8.4. Selection of computing tools -- 8.5. Difficulties in implementation and use -- 8.6. Evaluation -- 8.7. Conclusions -- Bibliography -- Index.