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Distributed Optimization on Riemannian Manifolds for multi-agent networks
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We consider the consensual distributed optimization problem in the Riemannian context. Specifically, the minimization of a sum of functions form is studied where each individual function in the sum is located at the node of a network. An algorithm, which is a direct generalization of the Euclidean case, to solve the problem is proposed. The convergence analysis is carried out in full detail for geodesically convex as well as non-convex functions. The algorithm is demonstrated using some standard applications which fit the presented framework.
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Cited by 2 Pith papers
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Distributed Riemannian Optimization in Geodesically Non-convex Environments
Riemannian diffusion adaptation provably reaches approximate consensus and first-order stationarity for geodesically non-convex costs, with linear convergence under the Riemannian PL condition.
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Decentralized projected Riemannian stochastic recursive momentum method for nonconvex optimization
DPRSRM is a single-loop decentralized stochastic recursive momentum method with gradient tracking on compact submanifolds, claimed to reach ε-stationarity with O(ε^{-3/2}) oracle complexity.
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