Non-convex self-concordant functions enable regularized Newton and adaptive algorithms to achieve epsilon-approximate first-order stationary points in O(epsilon^{-2}) iterations with global convergence guarantees.
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Non-Convex Self-Concordant Functions: Practical Algorithms and Complexity Analysis
Non-convex self-concordant functions enable regularized Newton and adaptive algorithms to achieve epsilon-approximate first-order stationary points in O(epsilon^{-2}) iterations with global convergence guarantees.