ZOPPA at fixed positive temperature converges under minimal assumptions by acting as an exact proximal point method on a smoothed objective, with explicit connections back to the original function and convergence for its sampled version.
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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.
Presents a model-based proximal framework for adaptive momentum in first-order optimizers by using a two-plane approximation of the objective to dynamically set the memory coefficient online.
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Convergence of zeroth-order proximal point algorithms in the high-temperature regime
ZOPPA at fixed positive temperature converges under minimal assumptions by acting as an exact proximal point method on a smoothed objective, with explicit connections back to the original function and convergence for its sampled version.
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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.
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Adaptive Memory Momentum via a Model-Based Framework for Deep Learning Optimization
Presents a model-based proximal framework for adaptive momentum in first-order optimizers by using a two-plane approximation of the objective to dynamically set the memory coefficient online.