The design of efficient and generic algorithms for solving combinatorial optimization problems has been an active field of research for many years. Standard exact solving approaches are based on a clever and complete enumeration of the solution set. A critical and non-trivial design choice with such methods is the branching strategy, directing how the search is performed. The last decade has shown an increasing interest in the design of machine learning-based heuristics to solve combinatorial optimization problems. The goal is to leverage knowledge from historical data to solve similar new instances of a problem. Used alone, such heuristics are only able to provide approximate solutions efficiently, but cannot prove optimality nor bounds on their solution. Recent works have shown that reinforcement learning can be successfully used for driving the search phase of constraint programming (CP) solvers. However, it has also been shown that this hybridization is challenging to build, as standard CP frameworks do not natively include machine learning mechanisms, leading to some sources of inefficiencies. This paper presents the proof of concept for SeaPearl, a new CP solver implemented in Julia, that supports machine learning routines in order to learn branching decisions using reinforcement learning. Support for modeling the learning component is also provided. We illustrate the modeling and solution performance of this new solver on two problems. Although not yet competitive with industrial solvers, SeaPearl aims to provide a flexible and open-source framework in order to facilitate future research in the hybridization of constraint programming and machine learning.
Reinforcement learning for adaptive interior point methods in convex quadratic programming
Department of Aerospace Engineering, Institute of Applied Mathematics and Scientific Computing, University of the Bundeswehr Munich（航空航天工程系、应用数学与科学计算研究所、联邦国防军大学穆尔尼奇分校）
A User Manual for cuHALLaR: A GPU Accelerated Low-Rank Semidefinite Programming Solver
H. M. Stewart School of Industrial and Systems Engineering, Georgia Tech（H.M. 斯图尔特工业与系统工程学院，佐治亚理工学院）；School of Applied Mathematics, FGV（应用数学学院，FGV）；Department of Combinatorics and Optimization, University of Waterloo（组合优化系，滑铁卢大学）
Speeding Up Mixed-Integer Programming Solvers with Sparse Learning for Branching
H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology（佐治亚理工学院 H. Milton Stewart 工业与系统工程系）；School of Chemical & Biomolecular Engineering, Georgia Institute of Technology（佐治亚理工学院化学与生物分子工程学院）
Optimizing Nursing Care Taxi Dispatch Leveraging Integer Linear Programming Solvers and Machine Learning
Graduate School of Information Science and Technology, The University of Osaka（信息科学与技术研究生学校，大阪大学）；Information Networking（信息网络）；RIKEN R-CCS（理化学研究所R-CCS）；Department of Computer and Control Engineering, Tanta University（计算机与控制工程系，塔塔大学）
