Cutting Plane Methods and Subgradient Methods
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Hyperheuristics in Logistics Kassem Danach
Hyperheuristics in Logistics Kassem Danach To cite this version: Kassem Danach. Hyperheuristics in Logistics. Combinatorics [math.CO]. Ecole Centrale de Lille, 2016. English. NNT : 2016ECLI0025. tel-01485160 HAL Id: tel-01485160 https://tel.archives-ouvertes.fr/tel-01485160 Submitted on 8 Mar 2017 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. No d’ordre: 315 Centrale Lille THÈSE présentée en vue d’obtenir le grade de DOCTEUR en Automatique, Génie Informatique, Traitement du Signal et des Images par Kassem Danach DOCTORAT DELIVRE PAR CENTRALE LILLE Hyperheuristiques pour des problèmes d’optimisation en logistique Hyperheuristics in Logistics Soutenue le 21 decembre 2016 devant le jury d’examen: President: Pr. Laetitia Jourdan Université de Lille 1, France Rapporteurs: Pr. Adnan Yassine Université du Havre, France Dr. Reza Abdi University of Bradford, United Kingdom Examinateurs: Pr. Saïd Hanafi Université de Valenciennes, France Dr. Abbas Tarhini Lebanese American University, Lebanon Dr. Rahimeh Neamatian Monemin University Road, United Kingdom Directeur de thèse: Pr. Frédéric Semet Ecole Centrale de Lille, France Co-encadrant: Dr. Shahin Gelareh Université de l’ Artois, France Invited Professor: Dr. Wissam Khalil Université Libanais, Lebanon Thèse préparée dans le Laboratoire CRYStAL École Doctorale SPI 072 (EC Lille) 2 Acknowledgements Firstly, I would like to express my sincere gratitude to my advisor Prof. -
A Branch-And-Price Approach with Milp Formulation to Modularity Density Maximization on Graphs
A BRANCH-AND-PRICE APPROACH WITH MILP FORMULATION TO MODULARITY DENSITY MAXIMIZATION ON GRAPHS KEISUKE SATO Signalling and Transport Information Technology Division, Railway Technical Research Institute. 2-8-38 Hikari-cho, Kokubunji-shi, Tokyo 185-8540, Japan YOICHI IZUNAGA Information Systems Research Division, The Institute of Behavioral Sciences. 2-9 Ichigayahonmura-cho, Shinjyuku-ku, Tokyo 162-0845, Japan Abstract. For clustering of an undirected graph, this paper presents an exact algorithm for the maximization of modularity density, a more complicated criterion to overcome drawbacks of the well-known modularity. The problem can be interpreted as the set-partitioning problem, which reminds us of its integer linear programming (ILP) formulation. We provide a branch-and-price framework for solving this ILP, or column generation combined with branch-and-bound. Above all, we formulate the column gen- eration subproblem to be solved repeatedly as a simpler mixed integer linear programming (MILP) problem. Acceleration tech- niques called the set-packing relaxation and the multiple-cutting- planes-at-a-time combined with the MILP formulation enable us to optimize the modularity density for famous test instances in- cluding ones with over 100 vertices in around four minutes by a PC. Our solution method is deterministic and the computation time is not affected by any stochastic behavior. For one of them, column generation at the root node of the branch-and-bound tree arXiv:1705.02961v3 [cs.SI] 27 Jun 2017 provides a fractional upper bound solution and our algorithm finds an integral optimal solution after branching. E-mail addresses: (Keisuke Sato) [email protected], (Yoichi Izunaga) [email protected]. -
Optimal Placement by Branch-And-Price
Optimal Placement by Branch-and-Price Pradeep Ramachandaran1 Ameya R. Agnihotri2 Satoshi Ono2;3;4 Purushothaman Damodaran1 Krishnaswami Srihari1 Patrick H. Madden2;4 SUNY Binghamton SSIE1 and CSD2 FAIS3 University of Kitakyushu4 Abstract— Circuit placement has a large impact on all aspects groups of up to 36 elements. The B&P approach is based of performance; speed, power consumption, reliability, and cost on column generation techniques and B&B. B&P has been are all affected by the physical locations of interconnected applied to solve large instances of well known NP-Complete transistors. The placement problem is NP-Complete for even simple metrics. problems such as the Vehicle Routing Problem [7]. In this paper, we apply techniques developed by the Operations We have tested our approach on benchmarks with known Research (OR) community to the placement problem. Using optimal configurations, and also on problems extracted from an Integer Programming (IP) formulation and by applying a the “final” placements of a number of recent tools (Feng Shui “branch-and-price” approach, we are able to optimally solve 2.0, Dragon 3.01, and mPL 3.0). We find that suboptimality is placement problems that are an order of magnitude larger than those addressed by traditional methods. Our results show that rampant: for optimization windows of nine elements, roughly suboptimality is rampant on the small scale, and that there is half of the test cases are suboptimal. As we scale towards merit in increasing the size of optimization windows used in detail windows with thirtysix elements, we find that roughly 85% of placement. -
Branch-And-Bound Experiments in Convex Nonlinear Integer Programming
Noname manuscript No. (will be inserted by the editor) More Branch-and-Bound Experiments in Convex Nonlinear Integer Programming Pierre Bonami · Jon Lee · Sven Leyffer · Andreas W¨achter September 29, 2011 Abstract Branch-and-Bound (B&B) is perhaps the most fundamental algorithm for the global solution of convex Mixed-Integer Nonlinear Programming (MINLP) prob- lems. It is well-known that carrying out branching in a non-simplistic manner can greatly enhance the practicality of B&B in the context of Mixed-Integer Linear Pro- gramming (MILP). No detailed study of branching has heretofore been carried out for MINLP, In this paper, we study and identify useful sophisticated branching methods for MINLP. 1 Introduction Branch-and-Bound (B&B) was proposed by Land and Doig [26] as a solution method for MILP (Mixed-Integer Linear Programming) problems, though the term was actually coined by Little et al. [32], shortly thereafter. Early work was summarized in [27]. Dakin [14] modified the branching to how we commonly know it now and proposed its extension to convex MINLPs (Mixed-Integer Nonlinear Programming problems); that is, MINLP problems for which the continuous relaxation is a convex program. Though a very useful backbone for ever-more-sophisticated algorithms (e.g., Branch- and-Cut, Branch-and-Price, etc.), the basic B&B algorithm is very elementary. How- Pierre Bonami LIF, Universit´ede Marseille, 163 Av de Luminy, 13288 Marseille, France E-mail: [email protected] Jon Lee Department of Industrial and Operations Engineering, University -
Cocircuits of Vector Matroids
RICE UNIVERSITY Cocircuits of Vector Matroids by John David Arellano A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE Master of Arts APPROVED, THESIS COMMITTEE: ?~ Ill::f;Cks, Chair Associate Professor of Computational and Applied Mathematics ~~~~ Richard A. Tapia Maxfield-Oshman Professor of Engineering University Professor of Computational and Applied Mathematics Wotao Yin Assistant Professor of Computational and Applied Mathematics Houston, Texas August, 2011 ABSTRACT Cocircuits of Vector Matroids by John David Arellano In this thesis, I present a set covering problem (SCP) formulation of the matroid cogirth problem, finding the cardinality of the smallest cocircuit of a matroid. Ad dressing the matroid cogirth problem can lead to significantly enhancing the design process of sensor networks. The solution to the matroid cogirth problem provides the degree of redundancy of the corresponding sensor network, and allows for the evalu ation of the quality of the network. I provide an introduction to matroids, and their relation to the degree of redundancy problem. I also discuss existing methods devel oped to solve the matroid cogirth problem and the SCP. Computational results are provided to validate a branch-and-cut algorithm that addresses the SCP formulation. Acknowledgments I would like to thank my parents and family for the love and support throughout my graduate career. I would like to thank my advisor, Dr. Illya Hicks, and the rest of my committee for their guidance and support. I would also like to thank Dr. Maria Cristina Villalobos for her guidance and support during my undergraduate career. A thanks also goes to Nabor Reyna, Dr. -
Mathematical Programming Lecture 19 OR 630 Fall 2005 November 1, 2005 Notes by Nico Diener
Mathematical Programming Lecture 19 OR 630 Fall 2005 November 1, 2005 Notes by Nico Diener Column and Constraint Generation Idea The column and constraint generation algorithms exploit the fact that the revised simplex algorithm only needs very limited access to data of the LP problem to solve some large-scale LPs. Illustration The one-dimensional cutting-stock problem (Chapter 6 of Bertsimas and Tsit- siklis, Chapters 13 and 26 of Chvatal). Problem A paper manufacturer produces paper in large rolls of width W and has a demand for narrow rolls of widths say w1, ..., wn, where 0 < wi < W . The demand is for bi rolls of width wi. Figure 1: We want to use as few large rolls as possible to satisfy the demand. We consider patterns, i.e., ways of cutting a large roll into smaller rolls: each pattern j corresponds to a vector m aj ∈ R with aij equal to the number of rolls of width wi in pattern j. 1 0 0 Example: a = 1 . 0 . . 1 If we could list all patterns say 1, 2, ..., N (N can be very large), then the problem would become: PN min j=1 xj PN j=1 ajxj = b, (1) xj ≥ 0 ∀j. Actually: We want xj, the number of rolls cut in pattern j, integer but we are just going to consider the LP relaxation. Problem N is large and A (the matrix made of columns aj) is known only implicitly: m PN Any vector a ∈ R satisfying i=1 wiai ≤ W , ai ≥ 0 and integer, defines a column of A. -
Generic Branch-Cut-And-Price
Generic Branch-Cut-and-Price Diplomarbeit bei PD Dr. M. L¨ubbecke vorgelegt von Gerald Gamrath 1 Fachbereich Mathematik der Technischen Universit¨atBerlin Berlin, 16. M¨arz2010 1Konrad-Zuse-Zentrum f¨urInformationstechnik Berlin, [email protected] 2 Contents Acknowledgments iii 1 Introduction 1 1.1 Definitions . .3 1.2 A Brief History of Branch-and-Price . .6 2 Dantzig-Wolfe Decomposition for MIPs 9 2.1 The Convexification Approach . 11 2.2 The Discretization Approach . 13 2.3 Quality of the Relaxation . 21 3 Extending SCIP to a Generic Branch-Cut-and-Price Solver 25 3.1 SCIP|a MIP Solver . 25 3.2 GCG|a Generic Branch-Cut-and-Price Solver . 27 3.3 Computational Environment . 35 4 Solving the Master Problem 39 4.1 Basics in Column Generation . 39 4.1.1 Reduced Cost Pricing . 42 4.1.2 Farkas Pricing . 43 4.1.3 Finiteness and Correctness . 44 4.2 Solving the Dantzig-Wolfe Master Problem . 45 4.3 Implementation Details . 48 4.3.1 Farkas Pricing . 49 4.3.2 Reduced Cost Pricing . 52 4.3.3 Making Use of Bounds . 54 4.4 Computational Results . 58 4.4.1 Farkas Pricing . 59 4.4.2 Reduced Cost Pricing . 65 5 Branching 71 5.1 Branching on Original Variables . 73 5.2 Branching on Variables of the Extended Problem . 77 5.3 Branching on Aggregated Variables . 78 5.4 Ryan and Foster Branching . 79 i ii Contents 5.5 Other Branching Rules . 82 5.6 Implementation Details . 85 5.6.1 Branching on Original Variables . 87 5.6.2 Ryan and Foster Branching . -
A Branch and Price Approach to the K-Clustering Minimum Biclique Completion Problem
A Branch and Price Approach to the k-Clustering Minimum Biclique Completion Problem Stefano Gualandia,1, Francesco Maffiolib, Claudio Magnic aDipartimento di Matematica, Universit`adegli Studi di Pavia, Via Ferrata 1, 27100, Pavia, Italy bPolitecnico di Milano, Dipartimento di Elettronica e Informazione, Piazza Leonardo da Vinci 32, 20133 Milano, Italy cMax Planck Institute for Computer Science, Department 1: Algorithms and Complexity, Campus E1 4, 66123 Saarbr¨ucken, Germany Abstract Given a bipartite graph G = (S, T, E), we consider the problem of finding k bipartite subgraphs, called ”clusters”, such that each vertex i of S appears in exactly one of them, every vertex j of T appears in each cluster in which at least one of its neighbors appears, and the total number of edges needed to make each cluster complete (i.e., to become a biclique) is minimized. This problem is known as k-clustering Minimum Biclique Completion Problem and has been shown strongly NP-hard. It has applications in bundling channels for multicast transmissions. Given a set of demands of services from clients, the application consists of finding k multicast sessions that partition the set of demands. Each service has to belong to a single multicast session, while each client can appear in more sessions. We extend previous work by developing a Branch and Price algorithm that embeds a new metaheuristic based on Variable Neighborhood Infeasible Search and a non-trivial branching rule. The metaheuristic is also adapted to solve efficiently the pricing subproblem. In addition to the random instances used in the literature, we present structured instances generated using the MovieLens data set collected by the GroupLens Research Project. -
Column Generation for Linear and Integer Programming
Documenta Math. 65 Column Generation for Linear and Integer Programming George L. Nemhauser 2010 Mathematics Subject Classification: 90 Keywords and Phrases: Column generation, decomposition, linear programming, integer programming, set partitioning, branch-and- price 1 The beginning – linear programming Column generation refers to linear programming (LP) algorithms designed to solve problems in which there are a huge number of variables compared to the number of constraints and the simplex algorithm step of determining whether the current basic solution is optimal or finding a variable to enter the basis is done by solving an optimization problem rather than by enumeration. To the best of my knowledge, the idea of using column generation to solve linear programs was first proposed by Ford and Fulkerson [16]. However, I couldn’t find the term column generation in that paper or the subsequent two seminal papers by Dantzig and Wolfe [8] and Gilmore and Gomory [17,18]. The first use of the term that I could find was in [3], a paper with the title “A column generation algorithm for a ship scheduling problem”. Ford and Fulkerson [16] gave a formulation for a multicommodity maximum flow problem in which the variables represented path flows for each commodity. The commodities represent distinct origin-destination pairs and integrality of the flows is not required. This formulation needs a number of variables ex- ponential in the size of the underlying network since the number of paths in a graph is exponential in the size of the network. What motivated them to propose this formulation? A more natural and smaller formulation in terms of the number of constraints plus the numbers of variables is easily obtained by using arc variables rather than path variables. -
Cutting Plane and Subgradient Methods
Cutting Plane and Subgradient Methods John E. Mitchell Department of Mathematical Sciences RPI, Troy, NY 12180 USA October 12, 2009 Collaborators: Luc Basescu, Brian Borchers, Mohammad Oskoorouchi, Srini Ramaswamy, Kartik Sivaramakrishnan, Mike Todd Mitchell (RPI) Cutting Planes, Subgradients INFORMS TutORial 1 / 72 Outline 1 Interior Point Cutting Plane and Column Generation Methods Introduction MaxCut Interior point cutting plane methods Warm starting Theoretical results Stabilization 2 Cutting Surfaces for Semidefinite Programming Semidefinite Programming Relaxations of dual SDP Computational experience Theoretical results with conic cuts Condition number 3 Smoothing techniques and subgradient methods 4 Conclusions Mitchell (RPI) Cutting Planes, Subgradients INFORMS TutORial 2 / 72 Interior Point Cutting Plane and Column Generation Methods Outline 1 Interior Point Cutting Plane and Column Generation Methods Introduction MaxCut Interior point cutting plane methods Warm starting Theoretical results Stabilization 2 Cutting Surfaces for Semidefinite Programming Semidefinite Programming Relaxations of dual SDP Computational experience Theoretical results with conic cuts Condition number 3 Smoothing techniques and subgradient methods 4 Conclusions Mitchell (RPI) Cutting Planes, Subgradients INFORMS TutORial 3 / 72 Interior Point Cutting Plane and Column Generation Methods Introduction Outline 1 Interior Point Cutting Plane and Column Generation Methods Introduction MaxCut Interior point cutting plane methods Warm starting Theoretical results -
Branch-And-Price: Column Generation for Solving Huge Integer Programs ,Y
BranchandPrice Column Generation for y Solving Huge Integer Programs Cynthia Barnhart Ellis L Johnson George L Nemhauser Martin WPSavelsb ergh Pamela H Vance School of Industrial and Systems Engineering Georgia Institute of Technology Atlanta GA Abstract We discuss formulations of integer programs with a huge number of variables and their solution by column generation metho ds ie implicit pricing of nonbasic variables to generate new columns or to prove LP optimality at a no de of the branch andb ound tree We present classes of mo dels for whichthisapproach decomp oses the problem provides tighter LP relaxations and eliminates symmetryWethen discuss computational issues and implementation of column generation branchand b ound algorithms including sp ecial branching rules and ecientways to solvethe LP relaxation February Revised May Revised January Intro duction The successful solution of largescale mixed integer programming MIP problems re quires formulations whose linear programming LP relaxations give a go o d approxima Currently at MIT Currently at Auburn University This research has b een supp orted by the following grants and contracts NSF SES NSF and AFORS DDM NSF DDM NSF DMI and IBM MHV y An abbreviated and unrefereed version of this pap er app eared in Barnhart et al tion to the convex hull of feasible solutions In the last decade a great deal of attention has b een given to the branchandcut approach to solving MIPs Homan and Padb erg and Nemhauser and Wolsey give general exp ositions of this metho dology The basic -
IEEE Paper Template in A4 (V1)
I.J. Information Technology and Computer Science, 2015, 07, 28-34 Published Online June 2015 in MECS (http://www.mecs-press.org/) DOI: 10.5815/ijitcs.2015.07.04 Great Deluge Algorithm for the Linear Ordering Problem: The Case of Tanzanian Input-Output Table Amos Mathias Department of Science Mathematics and Technology Education, University of Dodoma, Box 523, Dodoma, Tanzania Email: [email protected] Allen R. Mushi Department of Mathematics, University of Dar es salaam, Box 35062, DSM, Tanzania Email: [email protected] Abstract—Given a weighted complete digraph, the Linear Since then, it has received considerable attention by Ordering Problem (LOP) consists of finding and acyclic many researchers and hence becoming a problem of tournament with maximum weight. It is sometimes referred to interest to date. LOP is found in a number of applications, as triangulation problem or permutation problem depending on such as Triangulation of Input-Output matrices in the context of its application. This study introduces an economics, Archaeological seriation, Minimizing total algorithm for LOP and applied for triangulation of Tanzanian Input-Output tables. The algorithm development process uses weighted completion time in one-machine scheduling, Great Deluge heuristic method. It is implemented using C++ Aggregation of Individual preferences, Grötschel, Junger programming language and tested on a personal computer with and Reinelt [5], ordering of teams in sports tournaments, 2.40GHZ speed processor. The algorithm has been able to Mushi [1] as well as Machine translation, Tromble [6]. triangulate the Tanzanian input-output tables of size 79×79 A number of algorithms for the LOP solution have within a reasonable time (1.17 seconds).