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|a Model Predictive Control
|h [electronic resource] :
|b Engineering Methods for Economists /
|c edited by Aris Daniilidis, Lars Grüne, Josef Haunschmied, Gernot Tragler.
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|a 1st ed. 2025.
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|a Cham :
|b Springer Nature Switzerland :
|b Imprint: Springer,
|c 2025.
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|a XII, 224 p. 148 illus., 124 illus. in color.
|b online resource.
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|a text file
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|a Dynamic Modeling and Econometrics in Economics and Finance,
|x 2363-8370 ;
|v 31
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|a Chapter 1. Multi-horizon MPC and Its Application to theIntegrated Power and Thermal Management ofElectrified Vehicles (Qiuhao Hu) -- Chapter 2. Data/Moment-Driven Approaches for FastPredictive Control of Collective Dynamics (Giacomo Albi) -- Chapter 3. Finite-Dimensional Receding Horizon Control ofLinear Time-Varying Parabolic PDEs: StabilityAnalysis and Model-Order Reduction (Behzad Azmi) -- Chapter 4. Solving Hybrid Model Predictive ControlProblems via a Mixed-Integer Approach (Iman Nodozi) -- Chapter 5. nMPyC – A Python Package for Solving OptimalControl Problems via Model Predictive Control (Jonas Schießl) -- Chapter 6. Controllability of Continuous Networks and aKernel-Based Learning Approximation (Michael Herty) -- Chapter 7. Economic Model Predictive Control as aSolution to Markov Decision Processes (Dirk Reinhardt) -- Chapter 8. Reinforcement Learning with Guarantees (Mario Zanon).
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| 520 |
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|a The book explores the field of model predictive control (MPC). It reports on the latest developments in MPC, current applications, and presents various subfields of MPC. The book features topics such as uncertain and stochastic MPC variants, learning and neural network approaches, easy-to-use numerical implementations as well as multi-agent systems and scheduling and coordination tasks. While MPC is rooted in engineering science, this book illustrates the potential of using MPC theory and methods in non-engineering sciences and applications such as economics, finance, and environmental sciences.
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|a Accessibility summary: This PDF does not fully comply with PDF/UA standards, but does feature limited screen reader support, described non-text content (images, graphs), bookmarks for easy navigation and searchable, selectable text. Users of assistive technologies may experience difficulty navigating or interpreting content in this document. We recognize the importance of accessibility, and we welcome queries about accessibility for any of our products. If you have a question or an access need, please get in touch with us at accessibilitysupport@springernature.com.
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|a No reading system accessibility options actively disabled
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|a Publisher contact for further accessibility information: accessibilitysupport@springernature.com
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|a Econometrics.
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|a Operations research.
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| 650 |
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|a Social sciences
|x Mathematics.
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| 650 |
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|a Stochastic processes.
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|a Control engineering.
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|a Quantitative Economics.
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|a Operations Research and Decision Theory.
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|a Mathematics in Business, Economics and Finance.
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| 650 |
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|a Stochastic Systems and Control.
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| 650 |
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|a Control and Systems Theory.
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| 700 |
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|a Daniilidis, Aris.
|e editor.
|4 edt
|4 http://id.loc.gov/vocabulary/relators/edt
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| 700 |
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|a Grüne, Lars.
|e editor.
|4 edt
|4 http://id.loc.gov/vocabulary/relators/edt
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| 700 |
1 |
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|a Haunschmied, Josef.
|e editor.
|4 edt
|4 http://id.loc.gov/vocabulary/relators/edt
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| 700 |
1 |
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|a Tragler, Gernot.
|e editor.
|4 edt
|4 http://id.loc.gov/vocabulary/relators/edt
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| 710 |
2 |
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|a SpringerLink (Online service)
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|t Springer Nature eBook
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| 776 |
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|i Printed edition:
|z 9783031852558
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| 776 |
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|i Printed edition:
|z 9783031852572
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| 776 |
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|i Printed edition:
|z 9783031852589
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| 830 |
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|a Dynamic Modeling and Econometrics in Economics and Finance,
|x 2363-8370 ;
|v 31
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| 856 |
4 |
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|u https://doi.org/10.1007/978-3-031-85256-5
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|a ZDB-2-ECF
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| 950 |
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|a Economics and Finance (SpringerNature-41170)
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| 950 |
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|a Economics and Finance (R0) (SpringerNature-43720)
|