Abstract
ABSTRACTIn this paper, we propose and analyse a path-based incremental target level algorithm for minimizing a constrained convex optimization on complete Riemannian manifolds with lower bounded sectional curvature, where the object function consists of the sum of a large number of component functions. This algorithm extends, to the context of Riemannian manifolds, an incremental subgradient method emplying a version of dynamic stepsize rule. Some convergence results and iteration-complexity bounds of the algorithm are established.
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