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Found 19 papers, 4 papers with code
Learning to Cut via Hierarchical Sequence/Set Model for Efficient Mixed-Integer Programming
Moreover, we observe that (P3) what order of selected cuts to prefer significantly impacts the efficiency of MILP solvers as well.
Machine Learning Insides OptVerse AI Solver: Design Principles and Applications
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.
Promoting Generalization for Exact Solvers via Adversarial Instance Augmentation
To tackle this problem, we propose a novel approach, which is called Adversarial Instance Augmentation and does not require to know the problem type for new instance generation, to promote data diversity for learning-based branching modules in the branch-and-bound (B&B) Solvers (AdaSolver).
Accelerate Presolve in Large-Scale Linear Programming via Reinforcement Learning
Specifically, we formulate the routine design task as a Markov decision process and propose an RL framework with adaptive action sequences to generate high-quality presolve routines efficiently.
A Deep Instance Generative Framework for MILP Solvers Under Limited Data Availability
In the past few years, there has been an explosive surge in the use of machine learning (ML) techniques to address combinatorial optimization (CO) problems, especially mixed-integer linear programs (MILPs).
A Circuit Domain Generalization Framework for Efficient Logic Synthesis in Chip Design
In particular, we notice that the runtime of the Resub and Mfs2 operators often dominates the overall runtime of LS optimization processes.
SGDP: A Stream-Graph Neural Network Based Data Prefetcher
This paper proposes a novel Stream-Graph neural network-based Data Prefetcher (SGDP).
HardSATGEN: Understanding the Difficulty of Hard SAT Formula Generation and A Strong Structure-Hardness-Aware Baseline
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
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.
Offline Reinforcement Learning with Adaptive Behavior Regularization
Offline reinforcement learning (RL) defines a sample-efficient learning paradigm, where a policy is learned from static and previously collected datasets without additional interaction with the environment.
LQoCo: Learning to Optimize Cache Capacity Overloading in Storage Systems
Cache plays an important role to maintain high and stable performance (i. e. high throughput, low tail latency and throughput jitter) in storage systems.
A Survey for Solving Mixed Integer Programming via Machine Learning
This paper surveys the trend of leveraging machine learning to solve mixed integer programming (MIP) problems.
Machine Learning Methods in Solving the Boolean Satisfiability Problem
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.
Yordle: An Efficient Imitation Learning for Branch and Bound
Combinatorial optimization problems have aroused extensive research interests due to its huge application potential.
Introduction to The Dynamic Pickup and Delivery Problem Benchmark -- ICAPS 2021 Competition
The Dynamic Pickup and Delivery Problem (DPDP) is an essential problem within the logistics domain.
Learning to Reformulate for Linear Programming
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.
An Improved Reinforcement Learning Algorithm for Learning to Branch
Similar to offline reinforcement learning, we initially train on the demonstration data to accelerate learning massively.
Learning-Aided Heuristics Design for Storage System
Computer systems such as storage systems normally require transparent white-box algorithms that are interpretable for human experts.
Learning to Optimize Industry-Scale Dynamic Pickup and Delivery Problems
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.
