7 papers · 1 filter
TOBYQA: A Time-Augmented Model-Based Method for Derivative-Free Optimization under Noise and Temporal Drift
Haoyu Yao, Pengcheng Xie
Derivative-free optimization (DFO) is challenging when the observation channel varies over time and evaluations are noisy. Conventional model-based methods assume stationary observ…
Parallel Model-Based Derivative-Free Optimization via Rank-Two KKT Updates
Donghan Wu, Pengcheng Xie
Derivative-free optimization (DFO) addresses unconstrained problems $\min_{\x\in\RR^n} f(\x)$ where is accessed only through a zeroth-order oracle. Model-based trust-region met…
Low-Rank KKT Updates and a Parallel Flipping Mechanism for Model-Based Derivative-Free Optimization
Donghan Wu, Pengcheng Xie
Model-based derivative-free optimization relies on quadratic interpolation, but maintaining these models typically requires linear system solves. We show that fo…
BUP-TR: Bayesian Underdetermined Projection Trust-Region Methods for Derivative-Free Optimization
Wei Hu, Pengcheng Xie, Ya-Xiang Yuan +1
Underdetermined quadratic interpolation is a central model-construction tool in model-based derivative-free trust-region methods: it limits sampling costs but leaves an affine fami…
MATRO: Metric-Aware Trust-Region Optimization with Fully Quadratic Models
Wei Hu, Pengcheng Xie, Ya-Xiang Yuan +1
Model-based derivative-free trust-region methods build local interpolation models and restrict trial steps to regions where those models are reliable. This paper studies the shape…
Distributed Gradient-Regularized Newton Method: Scheduled Consensus and O(epsilon^{-1}) Global Iteration Complexity
Wei Hu, Pengcheng Xie, Ya-Xiang Yuan +1
We propose DisGrem, a fully decentralized second-order method for convex consensus optimization over networks. Each agent solves a local Newton system with vanishing gradient-norm…