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Inspired by the CL optimization architecture which periodically selects the best optimal parameters from different layers, we solve the dual function in the physical layer and application layer separately.
A simple case study is taken to enforce the optimization architecture.
The multi-loop optimization architecture integrates both new algorithms and existent procedures from different fields.
Based on the Lagrange dual decomposition method and CL optimization architecture, the convex optimization problem is separately solved in the physical layer and the application layer.
Based on the Lagrange dual decomposition method and cross-layer (CL) optimization architecture, this convex optimization problem is separately solved in the physical layer and the application layer.
There have been some researches that depend on cross-layer (CL) optimization architecture to solve QoE-oriented optimization problems [14 16].
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Control and optimization architectures will enable network-wide coordination of these grid components in order to improve system efficiency and reliability and to limit greenhouse gas emissions.
These architectures are then embedded into a simulation-based optimization (SIMOPT) architecture to address both combinatorial character and significant data uncertainty.
Architecture of the first component of our framework based on the context Fig. 3 User needs-based optimization component architecture.
Fig. 2 Context-based optimization component architecture.
This paper documents the optimization of architecture in accordance with Bejan's Constructal theory.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com