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.
Benchmarking optimization software with performance profiles.Mathematical programming, 91(2):201–213, 2002
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An SDP relaxation combined with a rounding scheme solves the balanced minimum evolution phylogenetic inference problem and produces accurate trees on simulated and empirical data.
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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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Phylogenetic Inference under the Balanced Minimum Evolution Criterion via Semidefinite Programming
An SDP relaxation combined with a rounding scheme solves the balanced minimum evolution phylogenetic inference problem and produces accurate trees on simulated and empirical data.