In the second stage, in spite of the non-convex constraints, exploiting zero duality gap the problem is solved in the dual domain by means of a stochastic learning method.
Simulation results are provided in multiple scenarios and they show that: (i) the proposed algorithms outperform the existing schemes in terms of providing QoS requirements of RT users and maximizing sum network transmission rates; (ii) algorithms designed under total power constraint exploit the power resource much better than those under individual power constraints.
The core constraint exploits relationships between profile columns, prohibiting insertions or deletions within blocks, rather than pursuing improvements through refinement of the column scoring function.
These constraints are exploited to include requirements for perfect regulation, efficient disturbance rejection, and permissible actuator usage.
A soft penalty is less stringent, with penalties chosen such that solutions that break constraints are exploited earlier in the analysis, but become uncompetitive as an optimal solution is approached.
The chapter concludes that CP can design specialized algorithms for individual constraints or subsets of constraints, thereby exploiting substructure in the problem that OR methods are likely to miss.
As phase sparse constraint is exploited, the proposed approach has superior performances than the existing iterative projection approaches in terms of rapid convergence, smaller residual error, noise stability, and suitability in large-scale phase retrieval problems.
This work will be extended to a dynamic FARED with temporal coupling constraints to exploit the synergistic capability of accommodating the injection uncertainties as well as alleviating the unit ramp rate constraints, and dynamic FARED with optimal conventional unit on/off statuses decision-making will be considered.
Thereby, we identified possible environmental constraints when exploiting Alpine RE potentials and generated recommendations for future strategies on expanding RE.
It is important to match the plant performance closely to understand unit constraints and exploit the new catalyst capabilities to full extent.
