Rotosolve converges to ε-stationary points for smooth non-convex objectives and ε-suboptimal points under PL, with explicit worst-case rates in the finite-shot regime, outperforming or matching RCD in nuanced ways.
Available: https://proceedings.neurips.cc/paper files/ paper/2016/file/299570476c6f0309545110c592b6a63b-Paper.pdf
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One Coordinate at a Time: Convergence Guarantees for Rotosolve in Variational Quantum Algorithms
Rotosolve converges to ε-stationary points for smooth non-convex objectives and ε-suboptimal points under PL, with explicit worst-case rates in the finite-shot regime, outperforming or matching RCD in nuanced ways.