A new regularized Hessian-free Newton-type method for smooth convex optimization achieves global O(k^{-2}) convergence and local quadratic convergence in a variant, with practical speedups over prior methods.
Super-universal regularized newton method.SIAM Journal on Optimization, 34(1):27–56
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Workshop notes explain the hypotheses required for first-order optimality conditions in MPECs and how to classify models and prove those hypotheses in practice.
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A Regularized Hessian-Free Inexact Newton-Type Method with Global $\mathcal{O}(k^{-2})$ Convergence
A new regularized Hessian-free Newton-type method for smooth convex optimization achieves global O(k^{-2}) convergence and local quadratic convergence in a variant, with practical speedups over prior methods.
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Workshop notes explain the hypotheses required for first-order optimality conditions in MPECs and how to classify models and prove those hypotheses in practice.