Search Results for author: Dongdong Ge

Found 12 papers, 4 papers with code

A Robust Anchor-based Method for Multi-Camera Pedestrian Localization

1 code implementation25 Oct 2024 Wanyu Zhang, JiaQi Zhang, Dongdong Ge, Yu Lin, Huiwen Yang, Huikang Liu, Yinyu Ye

This paper addresses the problem of vision-based pedestrian localization, which estimates a pedestrian's location using images and camera parameters.

Position

Reward Learning From Preference With Ties

no code implementations5 Oct 2024 Jinsong Liu, Dongdong Ge, Ruihao Zhu

To address this, we propose the adoption of the generalized Bradley-Terry model -- the Bradley-Terry model with ties (BTT) -- to accommodate tied preferences, thus leveraging additional information.

Solving Integrated Process Planning and Scheduling Problem via Graph Neural Network Based Deep Reinforcement Learning

no code implementations2 Sep 2024 Hongpei Li, Han Zhang, Ziyan He, Yunkai Jia, Bo Jiang, Xiang Huang, Dongdong Ge

The Integrated Process Planning and Scheduling (IPPS) problem combines process route planning and shop scheduling to achieve high efficiency in manufacturing and maximize resource utilization, which is crucial for modern manufacturing systems.

Graph Neural Network Scheduling

ORLM: Training Large Language Models for Optimization Modeling

1 code implementation28 May 2024 Zhengyang Tang, Chenyu Huang, Xin Zheng, Shixi Hu, Zizhuo Wang, Dongdong Ge, Benyou Wang

We apply the data from OR-Instruct to various open-source LLMs of 7b size (termed as ORLMs), resulting in a significantly improved capability for optimization modeling.

Prompt Engineering

Decoupling Learning and Decision-Making: Breaking the $\mathcal{O}(\sqrt{T})$ Barrier in Online Resource Allocation with First-Order Methods

no code implementations11 Feb 2024 Wenzhi Gao, Chunlin Sun, Chenyu Xue, Dongdong Ge, Yinyu Ye

Online linear programming plays an important role in both revenue management and resource allocation, and recent research has focused on developing efficient first-order online learning algorithms.

Decision Making Management

A Homogenization Approach for Gradient-Dominated Stochastic Optimization

no code implementations21 Aug 2023 Jiyuan Tan, Chenyu Xue, Chuwen Zhang, Qi Deng, Dongdong Ge, Yinyu Ye

In this paper, we propose the stochastic homogeneous second-order descent method (SHSODM) for stochastic functions enjoying gradient dominance property based on a recently proposed homogenization approach.

Management Reinforcement Learning (RL) +2

Pre-trained Mixed Integer Optimization through Multi-variable Cardinality Branching

no code implementations21 May 2023 Yanguang Chen, Wenzhi Gao, Dongdong Ge, Yinyu Ye

In this paper, we propose a Pre-trained Mixed Integer Optimization framework (PreMIO) that accelerates online mixed integer program (MIP) solving with offline datasets and machine learning models.

Learning Theory

Stochastic Dimension-reduced Second-order Methods for Policy Optimization

no code implementations28 Jan 2023 Jinsong Liu, Chenghan Xie, Qi Deng, Dongdong Ge, Yinyu Ye

In this paper, we propose several new stochastic second-order algorithms for policy optimization that only require gradient and Hessian-vector product in each iteration, making them computationally efficient and comparable to policy gradient methods.

Policy Gradient Methods Second-order methods

DRSOM: A Dimension Reduced Second-Order Method

3 code implementations30 Jul 2022 Chuwen Zhang, Dongdong Ge, Chang He, Bo Jiang, Yuntian Jiang, Yinyu Ye

In this paper, we propose a Dimension-Reduced Second-Order Method (DRSOM) for convex and nonconvex (unconstrained) optimization.

From an Interior Point to a Corner Point: Smart Crossover

1 code implementation18 Feb 2021 Dongdong Ge, Chengwenjian Wang, Zikai Xiong, Yinyu Ye

The crossover method, which aims at deriving an optimal extreme point from a suboptimal solution (the output of a starting method such as interior-point methods or first-order methods), is crucial in this process.

Optimization and Control 90C05

Uncertainty Quantification for Demand Prediction in Contextual Dynamic Pricing

no code implementations16 Mar 2020 Yining Wang, Xi Chen, Xiangyu Chang, Dongdong Ge

In this paper, using the problem of demand function prediction in dynamic pricing as the motivating example, we study the problem of constructing accurate confidence intervals for the demand function.

Assortment Optimization Management +2

Interior-Point Methods Strike Back: Solving the Wasserstein Barycenter Problem

no code implementations NeurIPS 2019 Dongdong Ge, Haoyue Wang, Zikai Xiong, Yinyu Ye

Computing the Wasserstein barycenter of a set of probability measures under the optimal transport metric can quickly become prohibitive for traditional second-order algorithms, such as interior-point methods, as the support size of the measures increases.

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