no code implementations • 19 Apr 2024 • Jie Wang, Zhihai Wang, Xijun Li, Yufei Kuang, Zhihao Shi, Fangzhou Zhu, Mingxuan Yuan, Jia Zeng, Yongdong Zhang, Feng Wu
Moreover, we observe that (P3) what order of selected cuts to prefer significantly impacts the efficiency of MILP solvers as well.
no code implementations • 28 Mar 2024 • Fu Luo, Xi Lin, Zhenkun Wang, Xialiang Tong, Mingxuan Yuan, Qingfu Zhang
The end-to-end neural combinatorial optimization (NCO) method shows promising performance in solving complex combinatorial optimization problems without the need for expert design.
no code implementations • 18 Mar 2024 • Xufeng Yao, Haoyang Li, Tsz Ho Chan, Wenyi Xiao, Mingxuan Yuan, Yu Huang, Lei Chen, Bei Yu
In the domain of chip design, Hardware Description Languages (HDLs) play a pivotal role.
no code implementations • 14 Mar 2024 • Xihan Li, Xing Li, Lei Chen, Xing Zhang, Mingxuan Yuan, Jun Wang
Then, can circuits also be mastered by a a sufficiently large "circuit model", which can conquer electronic design tasks by simply predicting the next logic gate?
no code implementations • 6 Mar 2024 • Tsz Ho Chan, Wenyi Xiao, Junhua Huang, HuiLing Zhen, Guangji Tian, Mingxuan Yuan
Boolean Satisfiability problems are vital components in Electronic Design Automation, particularly within the Logic Equivalence Checking process.
1 code implementation • 27 Feb 2024 • RuiZhe Zhong, Junjie Ye, Zhentao Tang, Shixiong Kai, Mingxuan Yuan, Jianye Hao, Junchi Yan
First, we propose global circuit training to pre-train a graph auto-encoder that learns the global graph embedding from circuit netlist.
1 code implementation • 23 Feb 2024 • Fei Liu, Xi Lin, Zhenkun Wang, Qingfu Zhang, Xialiang Tong, Mingxuan Yuan
The results show that the unified model demonstrates superior performance in the eleven VRPs, reducing the average gap to around 5% from over 20% in the existing approach and achieving a significant performance boost on benchmark datasets as well as a real-world logistics application.
no code implementations • 19 Feb 2024 • Yu Zhang, Hui-Ling Zhen, Zehua Pei, Yingzhao Lian, Lihao Yin, Mingxuan Yuan, Bei Yu
In this paper, we propose a novel solver-layer adaptation (SoLA) method, where we introduce a solver as a new layer of the LLM to differentially guide solutions towards satisfiability.
no code implementations • 3 Feb 2024 • Zehua Pei, Hui-Ling Zhen, Mingxuan Yuan, Yu Huang, Bei Yu
In this work, we propose a Verilog generation framework, BetterV, which fine-tunes the large language models (LLMs) on processed domain-specific datasets and incorporates generative discriminators for guidance on particular design demands.
no code implementations • 11 Jan 2024 • Xijun Li, Fangzhou Zhu, Hui-Ling Zhen, Weilin Luo, Meng Lu, Yimin Huang, Zhenan Fan, Zirui Zhou, Yufei Kuang, Zhihai Wang, Zijie Geng, Yang Li, Haoyang Liu, Zhiwu An, Muming Yang, Jianshu Li, Jie Wang, Junchi Yan, Defeng Sun, Tao Zhong, Yong Zhang, Jia Zeng, Mingxuan Yuan, Jianye Hao, Jun Yao, Kun Mao
To this end, we present a comprehensive study on the integration of machine learning (ML) techniques into Huawei Cloud's OptVerse AI Solver, which aims to mitigate the scarcity of real-world mathematical programming instances, and to surpass the capabilities of traditional optimization techniques.
2 code implementations • 4 Jan 2024 • Fei Liu, Xialiang Tong, Mingxuan Yuan, Xi Lin, Fu Luo, Zhenkun Wang, Zhichao Lu, Qingfu Zhang
Recently, we have proposed a novel Algorithm Evolution using Large Language Model (AEL) framework for automatic algorithm design.
1 code implementation • 28 Dec 2023 • RuiZhe Zhong, Xingbo Du, Shixiong Kai, Zhentao Tang, Siyuan Xu, Hui-Ling Zhen, Jianye Hao, Qiang Xu, Mingxuan Yuan, Junchi Yan
Since circuit can be represented with HDL in a textual format, it is reasonable to question whether LLMs can be leveraged in the EDA field to achieve fully automated chip design and generate circuits with improved power, performance, and area (PPA).
2 code implementations • 26 Nov 2023 • Fei Liu, Xialiang Tong, Mingxuan Yuan, Qingfu Zhang
In this paper, we propose a novel approach called Algorithm Evolution using Large Language Model (AEL).
1 code implementation • 19 Oct 2023 • Fei Liu, Xi Lin, Zhenkun Wang, Shunyu Yao, Xialiang Tong, Mingxuan Yuan, Qingfu Zhang
It is also promising to see the operator only learned from a few instances can have robust generalization performance on unseen problems with quite different patterns and settings.
1 code implementation • 22 Aug 2023 • Zhihai Wang, Lei Chen, Jie Wang, Xing Li, Yinqi Bai, Xijun Li, Mingxuan Yuan, Jianye Hao, Yongdong Zhang, Feng Wu
In particular, we notice that the runtime of the Resub and Mfs2 operators often dominates the overall runtime of LS optimization processes.
1 code implementation • 25 May 2023 • Zhengyuan Shi, Hongyang Pan, Sadaf Khan, Min Li, Yi Liu, Junhua Huang, Hui-Ling Zhen, Mingxuan Yuan, Zhufei Chu, Qiang Xu
Circuit representation learning aims to obtain neural representations of circuit elements and has emerged as a promising research direction that can be applied to various EDA and logic reasoning tasks.
1 code implementation • 7 Apr 2023 • Yiyuan Yang, Rongshang Li, Qiquan Shi, Xijun Li, Gang Hu, Xing Li, Mingxuan Yuan
This paper proposes a novel Stream-Graph neural network-based Data Prefetcher (SGDP).
no code implementations • 4 Mar 2023 • Hui-Ling Zhen, Naixing Wang, Junhua Huang, Xinyue Huang, Mingxuan Yuan, Yu Huang
(2) Conflict-driven implication and justification have been applied to increase decision accuracy and solving efficiency.
no code implementations • 1 Mar 2023 • Fei Liu, Chengyu Lu, Lin Gui, Qingfu Zhang, Xialiang Tong, Mingxuan Yuan
Vehicle routing is a well-known optimization research topic with significant practical importance.
1 code implementation • 4 Feb 2023 • Yang Li, Xinyan Chen, Wenxuan Guo, Xijun Li, Wanqian Luo, Junhua Huang, Hui-Ling Zhen, Mingxuan Yuan, Junchi Yan
On top of the observations that industrial formulae exhibit clear community structure and oversplit substructures lead to the difficulty in semantic formation of logical structures, we propose HardSATGEN, which introduces a fine-grained control mechanism to the neural split-merge paradigm for SAT formula generation to better recover the structural and computational properties of the industrial benchmarks.
no code implementations • 1 Feb 2023 • Zhihai Wang, Xijun Li, Jie Wang, Yufei Kuang, Mingxuan Yuan, Jia Zeng, Yongdong Zhang, Feng Wu
Cut selection -- which aims to select a proper subset of the candidate cuts to improve the efficiency of solving MILPs -- heavily depends on (P1) which cuts should be preferred, and (P2) how many cuts should be selected.
no code implementations • 2 Sep 2022 • Zhengyuan Shi, Min Li, Yi Liu, Sadaf Khan, Junhua Huang, Hui-Ling Zhen, Mingxuan Yuan, Qiang Xu
This paper introduces SATformer, a novel Transformer-based approach for the Boolean Satisfiability (SAT) problem.
no code implementations • 26 Jul 2022 • Zeren Huang, WenHao Chen, Weinan Zhang, Chuhan Shi, Furui Liu, Hui-Ling Zhen, Mingxuan Yuan, Jianye Hao, Yong Yu, Jun Wang
Deriving a good variable selection strategy in branch-and-bound is essential for the efficiency of modern mixed-integer programming (MIP) solvers.
no code implementations • 21 Mar 2022 • Ji Zhang, Xijun Li, Xiyao Zhou, Mingxuan Yuan, Zhuo Cheng, Keji Huang, YiFan Li
Cache plays an important role to maintain high and stable performance (i. e. high throughput, low tail latency and throughput jitter) in storage systems.
no code implementations • 6 Mar 2022 • Jiayi Zhang, Chang Liu, Junchi Yan, Xijun Li, Hui-Ling Zhen, Mingxuan Yuan
This paper surveys the trend of leveraging machine learning to solve mixed integer programming (MIP) problems.
no code implementations • 2 Mar 2022 • Wenxuan Guo, Junchi Yan, Hui-Ling Zhen, Xijun Li, Mingxuan Yuan, Yaohui Jin
This paper reviews the recent literature on solving the Boolean satisfiability problem (SAT), an archetypal NP-complete problem, with the help of machine learning techniques.
no code implementations • 25 Feb 2022 • Jiahui Duan, Xialiang Tong, Fei Ni, Zhenan He, Lei Chen, Mingxuan Yuan
The bin packing problem exists widely in real logistic scenarios (e. g., packing pipeline, express delivery), with its goal to improve the packing efficiency and reduce the transportation cost.
no code implementations • 19 Jan 2022 • Jianye Hao, Jiawen Lu, Xijun Li, Xialiang Tong, Xiang Xiang, Mingxuan Yuan, Hankz Hankui Zhuo
The Dynamic Pickup and Delivery Problem (DPDP) is an essential problem within the logistics domain.
no code implementations • 17 Jan 2022 • Qingyu Qu, Xijun Li, Yunfan Zhou, Jia Zeng, Mingxuan Yuan, Jie Wang, Jinhu Lv, Kexin Liu, Kun Mao
Similar to offline reinforcement learning, we initially train on the demonstration data to accelerate learning massively.
no code implementations • 17 Jan 2022 • Xijun Li, Qingyu Qu, Fangzhou Zhu, Jia Zeng, Mingxuan Yuan, Kun Mao, Jie Wang
In the past decades, a serial of traditional operation research algorithms have been proposed to obtain the optimum of a given LP in a fewer solving time.
no code implementations • NeurIPS 2021 • Yi Ma, Xiaotian Hao, Jianye Hao, Jiawen Lu, Xing Liu, Tong Xialiang, Mingxuan Yuan, Zhigang Li, Jie Tang, Zhaopeng Meng
To address this problem, existing methods partition the overall DPDP into fixed-size sub-problems by caching online generated orders and solve each sub-problem, or on this basis to utilize the predicted future orders to optimize each sub-problem further.
no code implementations • 14 Jun 2021 • Yingtian Tang, Han Lu, Xijun Li, Lei Chen, Mingxuan Yuan, Jia Zeng
Computer systems such as storage systems normally require transparent white-box algorithms that are interpretable for human experts.
no code implementations • 28 May 2021 • Zeren Huang, Kerong Wang, Furui Liu, Hui-Ling Zhen, Weinan Zhang, Mingxuan Yuan, Jianye Hao, Yong Yu, Jun Wang
In the online A/B testing of the product planning problems with more than $10^7$ variables and constraints daily, Cut Ranking has achieved the average speedup ratio of 12. 42% over the production solver without any accuracy loss of solution.
no code implementations • 27 May 2021 • Xijun Li, Weilin Luo, Mingxuan Yuan, Jun Wang, Jiawen Lu, Jie Wang, Jinhu Lu, Jia Zeng
Our method is entirely data driven and thus adaptive, i. e., the relational representation of adjacent vehicles can be learned and corrected by ST-DDGN from data periodically.
no code implementations • 10 Aug 2020 • Longkang Li, Hui-Ling Zhen, Mingxuan Yuan, Jiawen Lu, XialiangTong, Jia Zeng, Jun Wang, Dirk Schnieders
In this paper, we propose a Bilevel Deep reinforcement learning Scheduler, \textit{BDS}, in which the higher level is responsible for exploring an initial global sequence, whereas the lower level is aiming at exploitation for partial sequence refinements, and the two levels are connected by a sliding-window sampling mechanism.
1 code implementation • 25 Feb 2020 • Qiquan Shi, Jiaming Yin, Jiajun Cai, Andrzej Cichocki, Tatsuya Yokota, Lei Chen, Mingxuan Yuan, Jia Zeng
This work proposes a novel approach for multiple time series forecasting.
no code implementations • 10 Dec 2019 • Yige Zhang, Aaron Yi Ding, Jorg Ott, Mingxuan Yuan, Jia Zeng, Kun Zhang, Weixiong Rao
In this paper, by leveraging the recently developed transfer learning techniques, we design a novel Telco position recovery framework, called TLoc, to transfer good models in the carefully selected source domains (those fine-grained small subareas) to a target one which originally suffers from poor localization accuracy.