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This research explores decentralized constrained optimization in time-varying directed networks to minimize locally known cost functions.

Decentralized Constrained Optimization Over Time-Varying Directed Networks via Subgradient Rescaling

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This research explores decentralized constrained optimization in time-varying directed networks to minimize locally known cost functions.
Proposed the SR-DFRP algorithm for decentralized optimization.
Utilized Polyak's random projection to address nonidentical constraints.
Employed subgradient rescaling with row-stochastic matrices to maintain balance in networks.
SR-DFRP algorithm converges almost surely to the optimal solution.
Demonstrated efficacy through simulations on facility location and image deblurring problems.

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Decentralized optimization aims to minimize costs in networks with multiple constraints, indicating a novel solution approach.

Lü et al. (2026) studied this question.

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