1 citations · 1 across the 6 of their papers we have counts for
7 papers
Privacy-Preserving Black-Box Optimization (PBBO): Theory and the Model-Based Algorithm DFOp
Pengcheng Xie
This paper focuses on solving unconstrained privacy-preserving black-box optimization (PBBO), its corresponding least Frobenius norm updating of quadratic models, and the different…
Model-Driven Subspaces for Large-Scale Optimization with Local Approximation Strategy
Yitong He, Pengcheng Xie
Solving large-scale optimization problems is a bottleneck and is very important for machine learning and multiple kinds of scientific problems. Subspace-based methods using the loc…
Objective Value Change and Shape-Based Accelerated Optimization for the Neural Network Approximation
Pengcheng Xie, Zihao Zhou, Zijian Zhou
This paper introduce a novel metric of an objective function f, we say VC (value change) to measure the difficulty and approximation affection when conducting an neural network app…
Amber Pruner: Leveraging N:M Activation Sparsity for Efficient Prefill in Large Language Models
Tai An, Ruwu Cai, Yanzhe Zhang +6
In the era of large language models (LLMs), N:M sparsity has emerged as a structured compression technique critical for accelerating inference. While prior work has primarily focus…
ReMU: Regional Minimal Updating for Model-Based Derivative-Free Optimization
Pengcheng Xie, Stefan M. Wild
Derivative-free optimization (DFO) problems are optimization problems where derivative information is unavailable or extremely difficult to obtain. Model-based DFO solvers have bee…
On the Relationship between -poisedness in Derivative-Free Optimization and Outliers in Local Outlier Factor
Qi Zhang, Pengcheng Xie
Derivative-free optimization (DFO) is a method that does not require the calculation of gradients or higher-order derivatives of the objective function, making it suitable for case…
Sufficient Conditions for Error Distance Reduction in the (\ell^2)-norm Trust Region between Minimizers of Local Nonconvex Multivariate Quadratic Approximates
Pengcheng Xie
This paper analyzes the sufficient conditions for distance reduction between minimizers of local nonconvex quadratic approximate functions with diagonal Hessian in the (\ell^2)-nor…