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Optimization in engineering sciences [[electronic resource] ] : exact methods / / Pierre Borne ... [et al.]
Optimization in engineering sciences [[electronic resource] ] : exact methods / / Pierre Borne ... [et al.]
Autore Borne Pierre
Pubbl/distr/stampa Hoboken, N.J., : ISTE Ltd/John Wiley and Sons Inc., 2013
Descrizione fisica 1 online resource (328 p.)
Disciplina 519.92
629.89
Altri autori (Persone) BornePierre
Collana ISTE
Soggetto topico Engineering mathematics
Mathematical optimization
Program transformation (Computer programming)
Algorithms
Systems engineering
ISBN 1-118-57789-2
1-299-14153-6
1-118-57775-2
1-118-57784-1
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto 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 problemChapter 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 problem3.2. 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 problem4.3. 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
Record Nr. UNINA-9910141493603321
Borne Pierre  
Hoboken, N.J., : ISTE Ltd/John Wiley and Sons Inc., 2013
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Optimization in engineering sciences : exact methods / / Pierre Borne ... [et al.]
Optimization in engineering sciences : exact methods / / Pierre Borne ... [et al.]
Autore Borne Pierre
Edizione [1st ed.]
Pubbl/distr/stampa Hoboken, N.J., : ISTE Ltd/John Wiley and Sons Inc., 2013
Descrizione fisica 1 online resource (328 p.)
Disciplina 519.92
629.89
Altri autori (Persone) BornePierre
Collana ISTE
Soggetto topico Engineering mathematics
Mathematical optimization
Program transformation (Computer programming)
Algorithms
Systems engineering
ISBN 9781118577899
1118577892
9781299141537
1299141536
9781118577752
1118577752
9781118577844
1118577841
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto 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 problemChapter 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 problem3.2. 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 problem4.3. 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
Record Nr. UNINA-9910806124003321
Borne Pierre  
Hoboken, N.J., : ISTE Ltd/John Wiley and Sons Inc., 2013
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui