Search Results for author: Mingxuan Yuan

Found 36 papers, 10 papers with code

Self-Improved Learning for Scalable Neural Combinatorial Optimization

no code implementations28 Mar 2024 Fu Luo, Xi Lin, Zhenkun Wang, Tong Xialiang, 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.

Circuit Transformer: End-to-end Circuit Design by Predicting the Next Gate

no code implementations14 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?

Hallucination

IB-Net: Initial Branch Network for Variable Decision in Boolean Satisfiability

no code implementations6 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.

Multi-Task Learning for Routing Problem with Cross-Problem Zero-Shot Generalization

no code implementations23 Feb 2024 Fei Liu, Xi Lin, 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.

Attribute Combinatorial Optimization +2

SoLA: Solver-Layer Adaption of LLM for Better Logic Reasoning

no code implementations19 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.

Logical Reasoning

BetterV: Controlled Verilog Generation with Discriminative Guidance

no code implementations3 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.

Text Generation

Machine Learning Insides OptVerse AI Solver: Design Principles and Applications

no code implementations11 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.

Decision Making Management

LLM4EDA: Emerging Progress in Large Language Models for Electronic Design Automation

1 code implementation28 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).

Answer Generation Chatbot

Algorithm Evolution Using Large Language Model

2 code implementations26 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).

Language Modelling Large Language Model

Large Language Model for Multi-objective Evolutionary Optimization

1 code implementation19 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.

Evolutionary Algorithms Language Modelling +3

A Circuit Domain Generalization Framework for Efficient Logic Synthesis in Chip Design

1 code implementation22 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.

Domain Generalization

DeepGate2: Functionality-Aware Circuit Representation Learning

1 code implementation25 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.

Representation Learning

SGDP: A Stream-Graph Neural Network Based Data Prefetcher

1 code implementation7 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).

Conflict-driven Structural Learning Towards Higher Coverage Rate in ATPG

no code implementations4 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.

Heuristics for Vehicle Routing Problem: A Survey and Recent Advances

no code implementations1 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.

HardSATGEN: Understanding the Difficulty of Hard SAT Formula Generation and A Strong Structure-Hardness-Aware Baseline

1 code implementation4 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.

Learning Cut Selection for Mixed-Integer Linear Programming via Hierarchical Sequence Model

no code implementations1 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.

SATformer: Transformer-Based UNSAT Core Learning

no code implementations2 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.

Multi-Task Learning

LQoCo: Learning to Optimize Cache Capacity Overloading in Storage Systems

no code implementations21 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.

Management

Machine Learning Methods in Solving the Boolean Satisfiability Problem

no code implementations2 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.

BIG-bench Machine Learning

A Data-Driven Column Generation Algorithm For Bin Packing Problem in Manufacturing Industry

no code implementations25 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.

Combinatorial Optimization

Learning to Reformulate for Linear Programming

no code implementations17 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.

A Hierarchical Reinforcement Learning Based Optimization Framework for Large-scale Dynamic Pickup and Delivery Problems

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.

Hierarchical Reinforcement Learning

Learning-Aided Heuristics Design for Storage System

no code implementations14 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.

reinforcement-learning Reinforcement Learning (RL)

Learning to Select Cuts for Efficient Mixed-Integer Programming

no code implementations28 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.

Multiple Instance Learning

Learning to Optimize Industry-Scale Dynamic Pickup and Delivery Problems

no code implementations27 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.

Graph Embedding Management +1

Bilevel Learning Model Towards Industrial Scheduling

no code implementations10 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.

Scheduling

Transfer Learning-Based Outdoor Position Recovery with Telco Data

no code implementations10 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.

Position Transfer Learning

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