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Chao Ding

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3 papers

2 author rows

NeurIPS Conference 2025 Conference Paper

A Regularized Newton Method for Nonconvex Optimization with Global and Local Complexity Guarantees

Yuhao Zhou
Jintao Xu
Bingrui Li
Chenglong Bao
Chao Ding
Jun Zhu

Finding an $\epsilon$-stationary point of a nonconvex function with a Lipschitz continuous Hessian is a central problem in optimization. Regularized Newton methods are a classical tool and have been studied extensively, yet they still face a trade‑off between global and local convergence. Whether a parameter-free algorithm of this type can simultaneously achieve optimal global complexity and quadratic local convergence remains an open question. To bridge this long-standing gap, we propose a new class of regularizers constructed from the current and previous gradients, and leverage the conjugate gradient approach with a negative curvature monitor to solve the regularized Newton equation. The proposed algorithm is adaptive, requiring no prior knowledge of the Hessian Lipschitz constant, and achieves a global complexity of $O(\epsilon^{-\frac{3}{2}})$ in terms of the second-order oracle calls, and $\tilde O(\epsilon^{-\frac{7}{4}})$ for Hessian-vector products, respectively. When the iterates converge to a point where the Hessian is positive definite, the method exhibits quadratic local convergence. Preliminary numerical results, including training the physics-informed neural networks, illustrate the competitiveness of our algorithm.

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IROS Conference 2024 Conference Paper

CSR: A Lightweight Crowdsourced Road Structure Reconstruction System for Autonomous Driving

Huayou Wang
Qingyao Liu
Jiazheng Wu
Kun Liu
Chao Ding
Xianpeng Lang
Changliang Xue

Highly accurate and robust vectorized reconstruction of road structures is crucial for autonomous vehicles. Traditional LiDAR-based methods require multiple processes and are often expensive, time-consuming, labor-intensive, and cumbersome. In this paper, we propose a lightweight crowdsourced road structure reconstruction system (termed CSR) that relies solely on online perceived semantic elements. Ambiguities and perceptual errors of semantic features and Global Navigation Satellite System (GNSS) global pose errors constitute the predominant challenge in achieving alignment across multi-trip data. To this end, a robust two-phased coarse-to-fine multi-trip alignment method is performed considering local geometric consistency, global topology consistency, intra-trip temporal consistency, and inter-trip consistency. Further, we introduce an incremental pose graph optimization framework with adaptive weight tuning ability to integrate pre-built road structures, currently perceived multi-trip semantic features, odometry, and GNSS, enabling accurate and robust incremental road structure reconstruction. CSR is highly automated, efficient, and scalable for large-scale autonomous driving scenarios, significantly expediting road structure production. We quantitatively and qualitatively validate the reconstruction performance of CSR in real-world scenes. CSR achieves centimeter-level accuracy commensurate with established LiDAR-based methods, concurrently boosting efficiency and reducing resource expenditure.

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AAAI Conference 2021 Conference Paper

THOR, Trace-based Hardware-driven Layer-Oriented Natural Gradient Descent Computation

Mengyun Chen
Kaixin Gao
Xiaolei Liu
Zidong Wang
Ningxi Ni
Qian Zhang
Lei Chen
Chao Ding

It is well-known that second-order optimizer can accelerate the training of deep neural networks, however, the huge computation cost of second-order optimization makes it impractical to apply in real practice. In order to reduce the cost, many methods have been proposed to approximate a second-order matrix. Inspired by KFAC, we propose a novel Trace-based Hardware-driven layer-ORiented Natural Gradient Descent Computation method, called THOR, to make the second-order optimization applicable in the real application models. Specifically, we gradually increase the update interval and use the matrix trace to determine which blocks of Fisher Information Matrix (FIM) need to be updated. Moreover, by resorting the power of hardware, we have designed a hardware-driven approximation method for computing FIM to achieve better performance. To demonstrate the effectiveness of THOR, we have conducted extensive experiments. The results show that training ResNet-50 on ImageNet with THOR only takes 66. 7 minutes to achieve a top-1 accuracy of 75. 9 % under an 8 Ascend 910 environment with MindSpore, a new deep learning computing framework. Moreover, with more computational resources, THOR can only takes 2. 7 minutes to 75. 9 % with 256 Ascend 910.

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