Theory of Suboptimal Decisions: Decomposition and Aggregation: Mathematics and its Applications, cartea 12
Autor A.A. Pervozvanskii, V.G. Gaitsgorien Limba Engleză Paperback – 14 dec 2011
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| Paperback (1) | 620.86 lei 6-8 săpt. | |
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| SPRINGER NETHERLANDS – 31 mai 1988 | 627.01 lei 6-8 săpt. |
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Specificații
ISBN-13: 9789401077750
ISBN-10: 9401077754
Pagini: 408
Ilustrații: XVIII, 384 p.
Dimensiuni: 160 x 240 x 21 mm
Greutate: 0.57 kg
Ediția:Softcover reprint of the original 1st ed. 1988
Editura: SPRINGER NETHERLANDS
Colecția Springer
Seria Mathematics and its Applications
Locul publicării:Dordrecht, Netherlands
ISBN-10: 9401077754
Pagini: 408
Ilustrații: XVIII, 384 p.
Dimensiuni: 160 x 240 x 21 mm
Greutate: 0.57 kg
Ediția:Softcover reprint of the original 1st ed. 1988
Editura: SPRINGER NETHERLANDS
Colecția Springer
Seria Mathematics and its Applications
Locul publicării:Dordrecht, Netherlands
Public țintă
ResearchCuprins
1: The Perturbation Method in Mathematical Programming.- 1.1. Formulation and peculiarities of problems.- 1.2. Perturbations in linear programs.- 1.3 Nonlinear programs: perturbations in objective functions.- 1.4. Necessary and sufficient conditions for an extremum. Quasiconvex and quasilinear programs.- 1.5. Perturbations in nonconvex programs.- 2: Approximate Decomposition and Aggregation for Finite Dimensional Deterministic Problems.- 2.1. Perturbed decomposable structures and two-level planning.- 2.2. Aggregation of activities.- 2.3 Weakly controllable input-output characteristics.- 2.4. Input-output analysis.- 2.5. Aggregation in optimization models based on input-output analysis.- 2.6. Aggregation in the interregional transportation problem with regard to price scales.- 2.7. Optimization of discrete dynamic systems.- 2.8. Control of weakly dynamic systems under state variable constraints.- 3: Singular Programs.- 3.1. Singularity and regularization in quasiconvex problems.- 3.2. The auxiliary problem in the singular case.- 3.3. An approximate aggregation of Markov chains with incomes.- 3.4. An approximation algorithm for Markov programming.- 3.5. An iterative algorithm for suboptimization.- 3.6. An artificial introduction of singular perturbations in compact inverse methods.- 4: The Perturbation Method in Stochastic Programming.- 4.1. One- and two-stage problems.- 4.2. Optimal control problems with small random perturbations.- 4.3. Discrete dynamic systems with weak or aggregatable controls. An asymptotic stochastic maximum principle.- 4.4. Sliding planning and suboptimal decomposition of operative control in a production system.- 4.5. Sliding planning on an infinite horizon.- 4.6. Control of weakly dynamic systems under random disturbances.- 5: Suboptimal Linear Regulator Design.- 5.1. The LQ problem. Suboptimal decomposition.- 5.2. Loss of controllability, singularity, and suboptimal aggregation.- 5.3. Examples of suboptimal regulator synthesis.- 5.4. Control of oscillatory systems.- 5.5. LQG problems.- 6: Nonlinear Optimal Control Problems.- 6.1. The maximum principle and smooth solutions.- 6.2. The general terminal problem.- 6.3. Difference approximations.- 6.4. Weak control (nonuniqueness of the reduced solution).- 6.5. Aggregation in a singular perturbed problem.- Related Literature.
Recenzii
'Therefore, this book can be recommended to everybody interested in mathematical systems analysis.' Operating Research 36:6 1992