A hybrid SCA, projected subgradient, and Lagrangian relaxation framework is proposed for nonconvex, nonsmooth CPT-based resource allocation, outperforming MATLAB's SQP in objective value for larger agent populations in simulations.
Goal-oriented semantic resource allocation with cumulative prospect theoretic agents,
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Optimization for Semantic-Aware Resource Allocation under CPT-based Utilities
A hybrid SCA, projected subgradient, and Lagrangian relaxation framework is proposed for nonconvex, nonsmooth CPT-based resource allocation, outperforming MATLAB's SQP in objective value for larger agent populations in simulations.