SelfSplit parallelization for mixed-integer linear programming

被引：1
|
作者：
Fischetti, Matteo [1 ]
Monaci, Michele [2 ]
Salvagnin, Domenico [1 ]
机构：
[1] Univ Padua, DEI, Padua, Italy
[2] Univ Bologna, DEI, Bologna, Italy
来源：
COMPUTERS & OPERATIONS RESEARCH | 2018年 / 93卷
关键词：
Parallel computing; Enumerative algorithms; Mixed-integer programming; Computational analysis; BOUND ALGORITHMS; BRANCH; COMMUNICATION; OPTIMIZATION;
D O I：
10.1016/j.cor.2018.01.011
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
SelfSplit is a simple static mechanism to convert a sequential tree-search code into a parallel one. In this paradigm, tree-search is distributed among a set of identical workers, each of which is able to autonomously determine-without any communication with the other workers-the job parts it has to process. SelfSplit already proved quite effective in parallelizing Constraint Programming solvers. In the present paper we investigate the performance of SelfSplit when applied to a Mixed-Integer Linear Programming (MILP) solver. Both ad-hoc and general purpose MILP codes have been considered. Computational results show that SelfSplit, in spite of its simplicity, can achieve good speedups even in the MILP context. (C) 2018 Elsevier Ltd. All rights reserved.
引用
收藏
页码：101 / 112
页数：12
相关论文
共 50 条
  • [41] A nonlinear mixed-integer programming approach for variable selection in linear regression model
    Roozbeh, Mahdi
    Babaie-Kafaki, Saman
    Aminifard, Zohre
    COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION, 2023, 52 (11) : 5434 - 5445
  • [42] Shell and tube heat exchanger design using mixed-integer linear programming
    Goncalves, Caroline de O.
    Costa, Andre L. H.
    Bagajewicz, Miguel J.
    AICHE JOURNAL, 2017, 63 (06) : 1907 - 1922
  • [43] Dragline operation modelling and task assignment based on mixed-integer linear programming
    Haoquan Liu
    Michael P. Kearney
    Kevin J. Austin
    Optimization and Engineering, 2018, 19 : 1005 - 1036
  • [44] Design of grounding systems in substations using a mixed-integer linear programming formulation
    Khodr, H. M.
    Salloum, G. A.
    Saraiva, J. T.
    Matos, M. A.
    ELECTRIC POWER SYSTEMS RESEARCH, 2009, 79 (01) : 126 - 133
  • [45] Optimal Load Control and Scheduling through Distributed Mixed-integer Linear Programming
    Yfantis, Vassilios
    Motsch, William
    Bach, Nico
    Wagner, Achim
    Ruskowski, Martin
    2022 30TH MEDITERRANEAN CONFERENCE ON CONTROL AND AUTOMATION (MED), 2022, : 920 - 926
  • [46] A Scalable Solution Methodology for Mixed-Integer Linear Programming Problems Arising in Automation
    Bragin, Mikhail A.
    Luh, Peter B.
    Yan, Bing
    Sun, Xiaorong
    IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING, 2019, 16 (02) : 531 - 541
  • [47] Robust Quadratic Programming with Mixed-Integer Uncertainty
    Mittal, Areesh
    Gokalp, Can
    Hanasusanto, Grani A.
    INFORMS JOURNAL ON COMPUTING, 2020, 32 (02) : 201 - 218
  • [48] Implementation of Mixed-Integer Programming on Embedded System
    Novak, Jakub
    Chalupa, Petr
    25TH DAAAM INTERNATIONAL SYMPOSIUM ON INTELLIGENT MANUFACTURING AND AUTOMATION, 2014, 2015, 100 : 1649 - 1656
  • [49] Approximate Multiparametric Mixed-Integer Convex Programming
    Malyuta, Danylo
    Acikmese, Behcet
    IEEE CONTROL SYSTEMS LETTERS, 2020, 4 (01): : 157 - 162
  • [50] A Primal Decomposition Method with Suboptimality Bounds for Distributed Mixed-Integer Linear Programming
    Camisa, Andrea
    Notarnicola, Ivano
    Notarstefano, Giuseppe
    2018 IEEE CONFERENCE ON DECISION AND CONTROL (CDC), 2018, : 3391 - 3396
12345