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Distributed Gradient Tracking for Unbalanced Optimization With Different Constraint Sets
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DOI：10.1109/TAC.2022.3192316.png)
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Abstract
En 中文
tracking methods have become popular for distributed optimization in recent years, partially because they achieve linear convergence using only a constant step-size for strongly convex optimization. In this article, we construct a counterexample on constrained optimization to show that direct extension of gradient tracking by using projections cannot guarantee the correctness. Then, we propose projected gradient tracking algorithms with diminishing step-sizes rather than a constant one for distributed strongly convex optimization with different constraint sets and unbalanced graphs. Our basic algorithm can achieve O(ln T/T ) convergence rate. Moreover, we design an epoch iteration scheme and improve the convergence rate as O(1/T ).
Keywords:
Optimization
Convergence
Directed graphs
Convex functions
Multi-agent systems
Linear programming
Heuristic algorithms
different constraint sets
distrib- uted optimization
gradient tracking
unbalanced graphs
Journal
IF:
7
Papers:
1.3W
Citations:
6.7W
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Cited Papers
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