Accelerating Exact Combinatorial Optimization via RL-based Initialization -- A Case Study in Scheduling

• URL: http://arxiv.org/abs/2308.11652v1
• Date: Sat, 19 Aug 2023 15:52:43 GMT
• Title: Accelerating Exact Combinatorial Optimization via RL-based Initialization -- A Case Study in Scheduling
• Authors: Jiaqi Yin and Cunxi Yu
• Abstract summary: This research aims to develop an innovative approach that employs machine learning (ML) for addressing optimization problems. We introduce a novel two-phase RL-to-ILP scheduling framework, which includes three steps: 1) solver as coarse-grain scheduler, 2) solution relaxation and 3) exact solving via ILP. Our framework demonstrates the same scheduling performance compared with using exact scheduling methods while achieving up to 128 $times$ speed improvements.
• Score: 1.3053649021965603
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Scheduling on dataflow graphs (also known as computation graphs) is an NP-hard problem. The traditional exact methods are limited by runtime complexity, while reinforcement learning (RL) and heuristic-based approaches struggle with determinism and solution quality. This research aims to develop an innovative approach that employs machine learning (ML) for addressing combinatorial optimization problems, using scheduling as a case study. The goal is to provide guarantees in optimality and determinism while maintaining the runtime cost of heuristic methods. Specifically, we introduce a novel two-phase RL-to-ILP scheduling framework, which includes three steps: 1) RL solver acts as coarse-grain scheduler, 2) solution relaxation and 3) exact solving via ILP. Our framework demonstrates the same scheduling performance compared with using exact scheduling methods while achieving up to 128 $\times$ speed improvements. This was conducted on actual EdgeTPU platforms, utilizing ImageNet DNN computation graphs as input. Additionally, the framework offers improved on-chip inference runtime and acceleration compared to the commercially available EdgeTPU compiler.

Related papers

• Accelerating Cutting-Plane Algorithms via Reinforcement Learning Surrogates [49.84541884653309]
  A current standard approach to solving convex discrete optimization problems is the use of cutting-plane algorithms. Despite the existence of a number of general-purpose cut-generating algorithms, large-scale discrete optimization problems continue to suffer from intractability. We propose a method for accelerating cutting-plane algorithms via reinforcement learning.
  arXiv  Detail & Related papers  (2023-07-17T20:11:56Z)
• Maximize to Explore: One Objective Function Fusing Estimation, Planning, and Exploration [87.53543137162488]
  We propose an easy-to-implement online reinforcement learning (online RL) framework called textttMEX. textttMEX integrates estimation and planning components while balancing exploration exploitation automatically. It can outperform baselines by a stable margin in various MuJoCo environments with sparse rewards.
  arXiv  Detail & Related papers  (2023-05-29T17:25:26Z)
• RESPECT: Reinforcement Learning based Edge Scheduling on Pipelined Coral Edge TPUs [12.952987240366781]
  This work presents a reinforcement learning (RL) based scheduling framework, which learns the behaviors of optimal optimization algorithms. RL generates near-optimal scheduling results with short solving runtime overhead. Our framework has demonstrated up to $sim2.5times$ real-world on-chip runtime inference speedups over the commercial compiler.
  arXiv  Detail & Related papers  (2023-04-10T17:22:12Z)
• Learning to Optimize Permutation Flow Shop Scheduling via Graph-based Imitation Learning [70.65666982566655]
  Permutation flow shop scheduling (PFSS) is widely used in manufacturing systems. We propose to train the model via expert-driven imitation learning, which accelerates convergence more stably and accurately. Our model's network parameters are reduced to only 37% of theirs, and the solution gap of our model towards the expert solutions decreases from 6.8% to 1.3% on average.
  arXiv  Detail & Related papers  (2022-10-31T09:46:26Z)
• A Heuristically Assisted Deep Reinforcement Learning Approach for Network Slice Placement [0.7885276250519428]
  We introduce a hybrid placement solution based on Deep Reinforcement Learning (DRL) and a dedicated optimization based on the Power of Two Choices principle. The proposed Heuristically-Assisted DRL (HA-DRL) allows to accelerate the learning process and gain in resource usage when compared against other state-of-the-art approaches.
  arXiv  Detail & Related papers  (2021-05-14T10:04:17Z)
• Learning to Optimize: A Primer and A Benchmark [94.29436694770953]
  Learning to optimize (L2O) is an emerging approach that leverages machine learning to develop optimization methods. This article is poised to be the first comprehensive survey and benchmark of L2O for continuous optimization.
  arXiv  Detail & Related papers  (2021-03-23T20:46:20Z)
• A Two-stage Framework and Reinforcement Learning-based Optimization Algorithms for Complex Scheduling Problems [54.61091936472494]
  We develop a two-stage framework, in which reinforcement learning (RL) and traditional operations research (OR) algorithms are combined together. The scheduling problem is solved in two stages, including a finite Markov decision process (MDP) and a mixed-integer programming process, respectively. Results show that the proposed algorithms could stably and efficiently obtain satisfactory scheduling schemes for agile Earth observation satellite scheduling problems.
  arXiv  Detail & Related papers  (2021-03-10T03:16:12Z)
• Geometric Deep Reinforcement Learning for Dynamic DAG Scheduling [8.14784681248878]
  In this paper, we propose a reinforcement learning approach to solve a realistic scheduling problem. We apply it to an algorithm commonly executed in the high performance computing community, the Cholesky factorization. Our algorithm uses graph neural networks in combination with an actor-critic algorithm (A2C) to build an adaptive representation of the problem on the fly.
  arXiv  Detail & Related papers  (2020-11-09T10:57:21Z)
• Combining Deep Learning and Optimization for Security-Constrained Optimal Power Flow [94.24763814458686]
  Security-constrained optimal power flow (SCOPF) is fundamental in power systems. Modeling of APR within the SCOPF problem results in complex large-scale mixed-integer programs. This paper proposes a novel approach that combines deep learning and robust optimization techniques.
  arXiv  Detail & Related papers  (2020-07-14T12:38:21Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.