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A Note on Inexact Condition for Cubic Regularized Newton's Method
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A Note on Inexact Condition for Cubic Regularized Newton's Method
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This note considers the inexact cubic-regularized Newton's method (CR), which has been shown in \cite{Cartis2011a} to achieve the same order-level convergence rate to a secondary stationary point as the exact CR \citep{Nesterov2006}. However, the inexactness condition in \cite{Cartis2011a} is not implementable due to its dependence on future iterates variable. This note fixes such an issue by proving the same convergence rate for nonconvex optimization under an inexact adaptive condition that depends on only the current iterate. Our proof controls the sufficient decrease of the function value over the total iterations rather than each iteration as used in the previous studies, which can be of independent interest in other contexts.
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Cited by 1 Pith paper
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On Second-Order Methods for Bilevel Optimization
A deterministic single-loop cubic regularized Newton method for NCSC bilevel optimization that attains the optimal O(ε^{-1.5}) SOSP rate without repeated lower-level solves.
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