John C. S. Lui

activity

20242026

collaborators

6 papers

math.OC2026

Optimal Convex Optimization with Inexact Second-Order Oracles

Lesi Chen, Chengchang Liu, Luo Luo +2

In this paper, we present a novel second-order method called Accelerated Inexact Newton Extragradient (AINE) for convex optimization using $I^´$ -inexact Hessians. We show that AINE…

cs.LG2026

On the Convergence of Stochastic Low-Rank Adaptation

Ru Wang, Chengchang Liu, John C. S. Lui

Low-rank adaptation (LoRA) optimizes $J (B, A) = L (W_{base} + s B A)$ over two adapters $B \in R^{m \times r}$ and $A \in R^{r \times n}$ that form a low-…

math.OC2026

Stochastic Non-Smooth Non-Convex Optimization with Decision-Dependent Distributions

Chengchang Liu, Zongqi Wan, Haishan Ye +1

We study stochastic zeroth-order optimization with decision-dependent distributions, where the sampling law depends on the current decision and only noisy function values are avail…

math.OC2026

Second-Order Bilevel Optimization with Accelerated Convergence Rates

Sheng Yang, Chengchang Liu, Lesi Chen +1

This paper studies second-order methods for nonconvex-strongly-convex bilevel optimization. We propose a novel fully second-order bilevel approximation method (FSBA) that achieves…

quant-ph2025

Quantum Algorithms for Projection-Free Sparse Convex Optimization

Jianhao He, John C. S. Lui

This paper considers the projection-free sparse convex optimization problem for the vector domain and the matrix domain, which covers a large number of important applications in ma…

math.OC2024

An Enhanced Levenberg--Marquardt Method via Gram Reduction

Chengchang Liu, Luo Luo, John C. S. Lui

This paper studied the problem of solving the system of nonlinear equations $F (x) = 0$ , where $F : R^{d} \to R^{d}$ . We propose Gram-Reduced Lev…