DOCSLIB.ORG

The SCIP Optimization Suite

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
The SCIP Optimization Suite The SCIP Optimization Suite Gerald Gamrath Zuse Institute Berlin EWO Seminar February 13, 2015 Gerald Gamrath (ZIB): The SCIP Optimization Suite 1 ZIB: Fast Algorithms – Fast Computers Konrad-Zuse-Zentrum für Informationstechnik Berlin I non-university research institute and computing center of the state of Berlin I Research Units: I numerical analysis and modeling I visualization and data analysis I optimization: energy–traffic–telecommunication–linear and nonlinear IP I scientific information systems I distributed algorithms and supercomputing I President: Martin Grötschel I more information: http://www.zib.de Gerald Gamrath (ZIB): The SCIP Optimization Suite 2 ZIB: Fast Algorithms – Fast Computers Konrad-Zuse-Zentrum für Informationstechnik Berlin I non-university research institute and computing center of the state of Berlin I Research Units: I numerical analysis and modeling I visualization and data analysis I optimization: energy–traffic–telecommunication–linear and nonlinear IP I scientific information systems I distributed algorithms and supercomputing I President: Martin Grötschel I more information: http://www.zib.de Gerald Gamrath (ZIB): The SCIP Optimization Suite 2 Outline SCIP –SolvingConstraintIntegerPrograms Constraint Integer Programming Solving Constraint Integer Programs History and Applications http://scip.zib.de Gerald Gamrath (ZIB): The SCIP Optimization Suite 3 What is a Constraint Integer Program? Mixed Integer Program Constraint Program Objective function: Objective function: . linear function . arbitrary function Feasible set: Feasible set: . described by linear constraints . given by arbitrary constraints Variable domains: Variable domains: . real or integer values . arbitrary (usually finite) min cT x min c(x) s.t. Ax ≤ b s.t. x ∈ F I C I (xI , xC ) ∈ Z × R (xI , xN ) ∈ Z × X Gerald Gamrath (ZIB): The SCIP Optimization Suite 4 What is a Constraint Integer Program? Constraint Integer Program Objective function: . linear function min cT x Feasible set: s.t. x ∈ F I C . described by arbitrary constraints (xI , xC ) ∈ Z × R Variable domains: Remark: . real or integer values I arbitrary objective or variables When all integer variables fixed: modeled by constraints . CIP becomes an LP Gerald Gamrath (ZIB): The SCIP Optimization Suite 5 CIP I SATisfiability problems I Pseudo-Boolean Optimization I Finite Domain SAT PBO FD CP I Constraint Programming I Constraint Integer Programming Relation to CP and MIP I Every MIP is a CIP. “MIP ( CIP” I Every CP over a finite domain space is a CIP. “FD ( CIP” Constraint Integer Programming I Mixed Integer Programs MIP Gerald Gamrath (ZIB): The SCIP Optimization Suite 6 CIP I Pseudo-Boolean Optimization I Finite Domain PBO FD CP I Constraint Programming I Constraint Integer Programming Relation to CP and MIP I Every MIP is a CIP. “MIP ( CIP” I Every CP over a finite domain space is a CIP. “FD ( CIP” Constraint Integer Programming I Mixed Integer Programs I SATisfiability problems MIP SAT Gerald Gamrath (ZIB): The SCIP Optimization Suite 6 CIP I Finite Domain FD CP I Constraint Programming I Constraint Integer Programming Relation to CP and MIP I Every MIP is a CIP. “MIP ( CIP” I Every CP over a finite domain space is a CIP. “FD ( CIP” Constraint Integer Programming I Mixed Integer Programs I SATisfiability problems MIP I Pseudo-Boolean Optimization SAT PBO Gerald Gamrath (ZIB): The SCIP Optimization Suite 6 CIP CP I Constraint Programming I Constraint Integer Programming Relation to CP and MIP I Every MIP is a CIP. “MIP ( CIP” I Every CP over a finite domain space is a CIP. “FD ( CIP” Constraint Integer Programming I Mixed Integer Programs I SATisfiability problems MIP I Pseudo-Boolean Optimization I Finite Domain SAT PBO FD Gerald Gamrath (ZIB): The SCIP Optimization Suite 6 CIP I Constraint Integer Programming Relation to CP and MIP I Every MIP is a CIP. “MIP ( CIP” I Every CP over a finite domain space is a CIP. “FD ( CIP” Constraint Integer Programming I Mixed Integer Programs I SATisfiability problems MIP I Pseudo-Boolean Optimization I Finite Domain SAT PBO FD CP I Constraint Programming Gerald Gamrath (ZIB): The SCIP Optimization Suite 6 Relation to CP and MIP I Every MIP is a CIP. “MIP ( CIP” I Every CP over a finite domain space is a CIP. “FD ( CIP” Constraint Integer Programming I Mixed Integer Programs CIP I SATisfiability problems MIP I Pseudo-Boolean Optimization I Finite Domain SAT PBO FD CP I Constraint Programming I Constraint Integer Programming Gerald Gamrath (ZIB): The SCIP Optimization Suite 6 Constraint Integer Programming I Mixed Integer Programs CIP I SATisfiability problems MIP I Pseudo-Boolean Optimization I Finite Domain SAT PBO FD CP I Constraint Programming I Constraint Integer Programming Relation to CP and MIP I Every MIP is a CIP. “MIP ( CIP” I Every CP over a finite domain space is a CIP. “FD ( CIP” Gerald Gamrath (ZIB): The SCIP Optimization Suite 6 CIP Solving Techniques Presolving Primal Heuristics Branch & Bound Cutting Planes Domain Propagation Conflict Analysis x1 x1 x2 x2 ⇒ x3 x3 x4 x4 Gerald Gamrath (ZIB): The SCIP Optimization Suite 7 How do we solve CIPs? MIP, CP, and SAT MIP I branch-and-bound I LP relaxation I cutting planes CP I domain propagation SCIP SAT I conflict analysis I periodic restarts Gerald Gamrath (ZIB): The SCIP Optimization Suite 8 SCIP’s Modular, Plugin-based Structure pscost random close mostinf gen relps dualfix vbound cuts cmir clique cost obbt flow leastinf cover uct cgmip restart dfs probing Branch zero gomory infer hybrid half ence Propa Separator estim gator pseudo obj implied strong full bounds esti Node cg strong mate selector cloud allfull redcost rapid intobj strong learn root odd mcf dfs vbound redcost cycle breadth rounding setppc sos1 first bfs Impli rins shifting pseudo super cations boolean indicator soc rootsol shift& quad rens default diving prop subnlp ratic sos2 random simple rounding or rounding orbi var xor Tree Cutpool tope bound pscost proximity diving trivial trysol non Dialog linear Conflict logicor oneopt twoopt Constraint Primal actcons linking octane under Handler Heuristic diving cover SCIP objpscost diving clique linear vbound knap sack Relax Event coef mutation local cross veclen diving branching zi roundover diving indi abs Expr. integral cator power NLP Interpr. int shifting dualval cumu bivar linesearch guided zero lative conjunc iate diving fixand dins objective tion diving infer disjunc and tion ipopt CppAD Pricer intdiving feaspump count bound ··· sols disjunc. fracdiving comp spx2 bnd onents spx xprs bound convert shift int wbo zpl qso ccg cip domcol sol dual none LP Presolver cnf infer rlp Reader fix gate msk pbm extract fzn grb clp trivial implics cpx intto ppm binary pip lp gms opb osil mps Gerald Gamrath (ZIB): The SCIP Optimization Suite 9 SCIP’s Modular, Plugin-based Structure pscost random mostinf relps cost leastinf Branch infer ence Propa Separator gator full Node strong selector cloud allfull strong setppc sos1 Impli pseudo super cations boolean indicator soc quad ratic sos2 or orbi var xor Tree Cutpool tope bound non Dialog linear Conflict logicor linking Constraint Primal actcons Handler SCIP Heuristic diving linear knap sack Relax Event indi abs Expr. integral cator power NLP Interpr. cumu bivar lative conjunc iate tion disjunc and tion Pricer count bound ··· sols disjunc. LP Presolver Reader Gerald Gamrath (ZIB): The SCIP Optimization Suite 9 SCIP’s Modular, Plugin-based Structure pscost random close mostinf gen relps dualfix vbound cuts cmir clique cost obbt flow leastinf cover uct cgmip restart dfs probing Branch zero gomory infer hybrid half ence Propa Separator estim gator pseudo obj implied strong full bounds esti Node cg strong mate selector cloud allfull redcost rapid intobj strong learn root odd mcf dfs vbound redcost cycle breadth rounding setppc sos1 first bfs Impli rins shifting pseudo super cations boolean indicator soc rootsol shift& quad rens default diving prop subnlp ratic sos2 random simple rounding or rounding orbi var xor Tree Cutpool tope bound pscost proximity diving trivial trysol non Dialog linear Conflict logicor oneopt twoopt Constraint Primal actcons linking octane under Handler Heuristic diving cover SCIP objpscost diving clique linear vbound knap sack Relax Event coef mutation local cross veclen diving branching zi roundover diving indi abs Expr. integral cator power NLP Interpr. int shifting dualval cumu bivar linesearch guided zero lative conjunc iate diving fixand dins objective tion diving infer disjunc and tion ipopt CppAD Pricer intdiving feaspump count bound ··· sols disjunc. fracdiving comp spx2 bnd onents spx xprs bound convert shift int wbo zpl qso ccg cip domcol sol dual none LP Presolver cnf infer rlp Reader fix gate msk pbm extract fzn grb clp trivial implics cpx intto ppm binary pip lp gms opb osil mps Gerald Gamrath (ZIB): The SCIP Optimization Suite 9 Some facts about SCIP I general setup I plugin based system I default plugins handle MIPs and nonconvex MINLPs I support for branch-and-price and custom relaxations I documentation and guidelines I more than 450 000 lines of C code, 20% documentation I 30 000 assertions, 4 000 debug messages I HowTos: plugins types, debugging, automatic testing I 11 examples illustrating the use of SCIP I active mailing list [email protected] (300 members) I interface and usability I user-friendly interactive shell I interfaces to AMPL, GAMS, ZIMPL, MATLAB, Python and Java I C++ wrapper classes I LP solvers: CLP, CPLEX, Gurobi, MOSEK, QSopt, SoPlex, Xpress I over 1 600 parameters and 15 emphasis settings I about 8000 downloads per year from 100+ countries Gerald Gamrath (ZIB): The SCIP Optimization Suite 10 (Some) Universities and Institutes using SCIP . RWTH Aaachen . UC Berkeley . Université de Montréal . Universität Bayreuth . Lehigh University . NIISI RAS . FU Berlin . University of Bristol . Université de Nantes . TU Berlin . Eötvös Loránd Tudományegyetem . The University of Newcastle . HU Berlin . Universidad de Buenos Aires . University of Nottingham . WIAS Berlin . Institut Français de Mécanique Avancée .
Load more

Recommended publications
  • An Algorithm Based on Semidefinite Programming for Finding Minimax
    Takustr. 7 Zuse Institute Berlin 14195 Berlin Germany BELMIRO P.M. DUARTE,GUILLAUME SAGNOL,WENG KEE WONG An algorithm based on Semidefinite Programming for finding minimax optimal designs ZIB Report 18-01 (December 2017) Zuse Institute Berlin Takustr. 7 14195 Berlin Germany Telephone: +49 30-84185-0 Telefax: +49 30-84185-125 E-mail: [email protected] URL: http://www.zib.de ZIB-Report (Print) ISSN 1438-0064 ZIB-Report (Internet) ISSN 2192-7782 An algorithm based on Semidefinite Programming for finding minimax optimal designs Belmiro P.M. Duarte a,b, Guillaume Sagnolc, Weng Kee Wongd aPolytechnic Institute of Coimbra, ISEC, Department of Chemical and Biological Engineering, Portugal. bCIEPQPF, Department of Chemical Engineering, University of Coimbra, Portugal. cTechnische Universität Berlin, Institut für Mathematik, Germany. dDepartment of Biostatistics, Fielding School of Public Health, UCLA, U.S.A. Abstract An algorithm based on a delayed constraint generation method for solving semi- infinite programs for constructing minimax optimal designs for nonlinear models is proposed. The outer optimization level of the minimax optimization problem is solved using a semidefinite programming based approach that requires the de- sign space be discretized. A nonlinear programming solver is then used to solve the inner program to determine the combination of the parameters that yields the worst-case value of the design criterion. The proposed algorithm is applied to find minimax optimal designs for the logistic model, the flexible 4-parameter Hill homoscedastic model and the general nth order consecutive reaction model, and shows that it (i) produces designs that compare well with minimax D−optimal de- signs obtained from semi-infinite programming method in the literature; (ii) can be applied to semidefinite representable optimality criteria, that include the com- mon A−; E−; G−; I− and D-optimality criteria; (iii) can tackle design problems with arbitrary linear constraints on the weights; and (iv) is fast and relatively easy to use.
    [Show full text]
  • Solving Mixed Integer Linear and Nonlinear Problems Using the SCIP Optimization Suite
    Takustraße 7 Konrad-Zuse-Zentrum D-14195 Berlin-Dahlem fur¨ Informationstechnik Berlin Germany TIMO BERTHOLD GERALD GAMRATH AMBROS M. GLEIXNER STEFAN HEINZ THORSTEN KOCH YUJI SHINANO Solving mixed integer linear and nonlinear problems using the SCIP Optimization Suite Supported by the DFG Research Center MATHEON Mathematics for key technologies in Berlin. ZIB-Report 12-27 (July 2012) Herausgegeben vom Konrad-Zuse-Zentrum f¨urInformationstechnik Berlin Takustraße 7 D-14195 Berlin-Dahlem Telefon: 030-84185-0 Telefax: 030-84185-125 e-mail: [email protected] URL: http://www.zib.de ZIB-Report (Print) ISSN 1438-0064 ZIB-Report (Internet) ISSN 2192-7782 Solving mixed integer linear and nonlinear problems using the SCIP Optimization Suite∗ Timo Berthold Gerald Gamrath Ambros M. Gleixner Stefan Heinz Thorsten Koch Yuji Shinano Zuse Institute Berlin, Takustr. 7, 14195 Berlin, Germany, fberthold,gamrath,gleixner,heinz,koch,[email protected] July 31, 2012 Abstract This paper introduces the SCIP Optimization Suite and discusses the ca- pabilities of its three components: the modeling language Zimpl, the linear programming solver SoPlex, and the constraint integer programming frame- work SCIP. We explain how these can be used in concert to model and solve challenging mixed integer linear and nonlinear optimization problems. SCIP is currently one of the fastest non-commercial MIP and MINLP solvers. We demonstrate the usage of Zimpl, SCIP, and SoPlex by selected examples, give an overview of available interfaces, and outline plans for future development. ∗A Japanese translation of this paper will be published in the Proceedings of the 24th RAMP Symposium held at Tohoku University, Miyagi, Japan, 27{28 September 2012, see http://orsj.or.
    [Show full text]
  • Linear Underestimators for Bivariate Functions with a Fixed Convexity Behavior
    Takustraße 7 Konrad-Zuse-Zentrum D-14195 Berlin-Dahlem fur¨ Informationstechnik Berlin Germany MARTIN BALLERSTEINy AND DENNIS MICHAELSz AND STEFAN VIGERSKE Linear Underestimators for bivariate functions with a fixed convexity behavior This work is part of the Collaborative Research Centre “Integrated Chemical Processes in Liquid Multiphase Systems” (CRC/Transregio 63 “InPROMPT”) funded by the German Research Foundation (DFG). Main parts of this work has been finished while the second author was at the Institute for Operations Research at ETH Zurich and financially supported by DFG through CRC/Transregio 63. The first and second author thank the DFG for its financial support. Thethirdauthor was supported by the DFG Research Center MATHEON Mathematics for key technologies and the Research Campus MODAL Mathematical Optimization and Data Analysis Laboratories in Berlin. y Eidgenossische¨ Technische, Hochschule Zurich,¨ Institut fur¨ Operations Research, Ramistrasse¨ 101, 8092 Zurich (Switzerland) z Technische Universitat¨ Dortmund, Fakultat¨ fur¨ Mathematik, M/518, Vogelpothsweg 87, 44227 Dortmund (Germany) ZIB-Report 13-02 (revised) (February 2015) Herausgegeben vom Konrad-Zuse-Zentrum fur¨ Informationstechnik Berlin Takustraße 7 D-14195 Berlin-Dahlem Telefon: 030-84185-0 Telefax: 030-84185-125 e-mail: [email protected] URL: http://www.zib.de ZIB-Report (Print) ISSN 1438-0064 ZIB-Report (Internet) ISSN 2192-7782 Technical Report Linear Underestimators for bivariate functions with a fixed convexity behavior∗ A documentation for the SCIP constraint handler cons bivariate Martin Ballersteiny Dennis Michaelsz Stefan Vigerskex February 23, 2015 y Eidgenossische¨ Technische Hochschule Zurich¨ Institut fur¨ Operations Research Ramistrasse¨ 101, 8092 Zurich (Switzerland) z Technische Universitat¨ Dortmund Fakultat¨ fur¨ Mathematik, M/518 Vogelpothsweg 87, 44227 Dortmund (Germany) x Zuse Institute Berlin Takustr.
    [Show full text]
  • Nonlinear Programming Techniques for Operative Planning in Large Drinking Water Networks Jens Burgschweiger1, Bernd Gnädig2 and Marc C
    14 The Open Applied Mathematics Journal, 2009, 3, 14-28 Open Access Nonlinear Programming Techniques for Operative Planning in Large Drinking Water Networks Jens Burgschweiger1, Bernd Gnädig2 and Marc C. Steinbach3,* 1Berliner Wasserbetriebe, Abt. NA-G/W, 10864 Berlin, Germany 2Düsseldorf, Germany 3Leibniz Universität Hannover, IfAM, Welfengarten 1, 30167 Hannover, Germany Abstract: Mathematical decision support for operative planning in water supply systems is highly desirable; it leads, however, to very difficult optimization problems. We propose a nonlinear programming approach that yields practically satisfactory operating schedules in acceptable computing time even for large networks. Based on a carefully designed model supporting gradient-based optimization algorithms, this approach employs a special initialization strategy for convergence acceleration, special minimum up and down time constraints together with pump aggregation to handle switching decisions, and several network reduction techniques for further speed-up. Results for selected application scenarios at Berliner Wasserbetriebe demonstrate the success of the approach. Keywords: Water supply, large-scale nonlinear programming, convergence acceleration, discrete decisions, network reduction. INTRODUCTION • experiments with various optimization models and methods [36, 37], Stringent requirements on cost effectiveness and environmental compatibility generate an increased demand • a first nonlinear programming (NLP) model for model-based decision support tools for designing and developed under GAMS [38], operating municipal water supply systems. This paper deals • numerical results for a substantially reduced with the minimum cost operation of drinking water network graph using (under GAMS) the SQP codes networks. Operative planning in water networks is difficult: CONOPT, SNOPT, and the augmented Lagrangian a sound mathematical model leads to nonlinear mixed- code MINOS.
    [Show full text]
  • 54 OP08 Abstracts
    54 OP08 Abstracts CP1 Dept. of Mathematics Improving Ultimate Convergence of An Aug- [email protected] mented Lagrangian Method Optimization methods that employ the classical Powell- CP1 Hestenes-Rockafellar Augmented Lagrangian are useful A Second-Derivative SQP Method for Noncon- tools for solving Nonlinear Programming problems. Their vex Optimization Problems with Inequality Con- reputation decreased in the last ten years due to the com- straints parative success of Interior-Point Newtonian algorithms, which are asymptotically faster. In the present research We consider a second-derivative 1 sequential quadratic a combination of both approaches is evaluated. The idea programming trust-region method for large-scale nonlin- is to produce a competitive method, being more robust ear non-convex optimization problems with inequality con- and efficient than its ”pure” counterparts for critical prob- straints. Trial steps are composed of two components; a lems. Moreover, an additional hybrid algorithm is defined, Cauchy globalization step and an SQP correction step. A in which the Interior Point method is replaced by the New- single linear artificial constraint is incorporated that en- tonian resolution of a KKT system identified by the Aug- sures non-accent in the SQP correction step, thus ”guiding” mented Lagrangian algorithm. the algorithm through areas of indefiniteness. A salient feature of our approach is feasibility of all subproblems. Ernesto G. Birgin IME-USP Daniel Robinson Department of Computer Science Oxford University [email protected]
    [Show full text]
  • Using SCIP to Solve Your Favorite Integer Optimization Problem
    Using SCIP to Solve Your Favorite Integer Optimization Problem Gregor Hendel, [email protected], www.zib.de/hendel Monash University, Melbourne March 12, 2019 Gregor Hendel{ Using SCIP 1/78 Zuse Institute Berlin { Fast Algorithms, Fast Computers A research institute and computing center of the State of Berlin with research units: • Numerical Analysis and Modeling • Visualization and Data Analysis • Optimization: Energy { Transportation { Health { Mathematical Optimization Methods • Scientific Information Systems • Computer Science and High Performance Computing Gregor Hendel{ Using SCIP 2/78 What is SCIP? SCIP (Solving Constraint Integer Programs) . • provides a full-scale MIP and MINLP solver, • is constraint based, • incorporates • MIP features (cutting planes, LP relaxation), and • MINLP features (spatial branch-and-bound, OBBT) • CP features (domain propagation), • SAT-solving features (conflict analysis, restarts), • is a branch-cut-and-price framework, • has a modular structure via plugins, • is free for academic purposes, • and is available in source-code under http://scip.zib.de ! Gregor Hendel{ Using SCIP 3/78 Meet the SCIP Team 31 active developers • 7 running Bachelor and Master projects • 16 running PhD projects • 8 postdocs and professors 4 development centers in Germany • ZIB: SCIP, SoPlex, UG, ZIMPL • TU Darmstadt: SCIP and SCIP-SDP • FAU Erlangen-N¨urnberg: SCIP • RWTH Aachen: GCG Many international contributors and users • more than 10 000 downloads per year from 100+ countries Careers • 10 awards for Masters and PhD theses:
    [Show full text]
  • Computational Aspects of Infeasibility Analysis in Mixed Integer Programming
    Takustr. 7 Zuse Institute Berlin 14195 Berlin Germany JAKOB WITZIG TIMO BERTHOLD STEFAN HEINZ Computational Aspects of Infeasibility Analysis in Mixed Integer Programming ZIB Report 19-54 (November 2019) Zuse Institute Berlin Takustr. 7 14195 Berlin Germany Telephone: +49 30-84185-0 Telefax: +49 30-84185-125 E-mail: [email protected] URL: http://www.zib.de ZIB-Report (Print) ISSN 1438-0064 ZIB-Report (Internet) ISSN 2192-7782 Computational Aspects of Infeasibility Analysis in Mixed Integer Programming Jakob Witzig,1 Timo Berthold,2 and Stefan Heinz3 1Zuse Institute Berlin, Takustr. 7, 14195 Berlin, Germany [email protected] 2Fair Isaac Germany GmbH, Stubenwald-Allee 19, 64625 Bensheim, Germany [email protected] 3Gurobi GmbH, Ulmenstr. 37–39, 60325 Frankfurt am Main, Germany [email protected] November 6, 2019 Abstract The analysis of infeasible subproblems plays an important role in solv- ing mixed integer programs (MIPs) and is implemented in most major MIP solvers. There are two fundamentally different concepts to gener- ate valid global constraints from infeasible subproblems. The first is to analyze the sequence of implications, obtained by domain propagation, that led to infeasibility. The result of this analysis is one or more sets of contradicting variable bounds from which so-called conflict constraints can be generated. This concept is called conflict graph analysis and has its origin in solving satisfiability problems and is similarly used in con- straint programming. The second concept is to analyze infeasible linear programming (LP) relaxations. Every ray of the dual LP provides a set of multipliers that can be used to generate a single new globally valid linear constraint.
    [Show full text]
  • Classical Benders Decomposition (KC203) Cluster: CPAIOR Chair: Jean-François Cordeau, HEC Montreal
    Sunday, May 29, 2016 9:00am - 10:00am Session: Classical Benders Decomposition (KC203) Cluster: CPAIOR Chair: Jean-François Cordeau, HEC Montreal Classical Benders Decomposition Jean-François Cordeau, HEC Montréal, [email protected] Benders decomposition is a mathematical decomposition technique designed to solve large- scale linear and mixed-integer programs comprising two sets of variables such that a more tractable subproblem is obtained when the values of some "complicating" variables are fixed. The technique works by projecting the original problem on the space of the complicating variables, and by using a cutting plane method where cuts are generated by solving the subproblem. The purpose of this masterclass is to introduce the basic Benders decomposition methodology, to present several computational refinements that have shown to improve performance, and to discuss some recent developments. ______________________________________________________________________________ 10:15am - 11:15am Session: Logic-Based Benders Decomposition (KC203) Cluster: CPAIOR Chair: John Hooker, Carnegie Mellon University Logic-Based Benders Decomposition John Hooker, Carnegie Mellon University, [email protected] This is a tutorial on logic-based Benders decomposition, which generalizes the classical Benders method so as to accommodate an arbitrary subproblem rather than only a linear or continuous nonlinear programming problem. This allows one to apply Benders profitably to any problem that simplifies or decouples when certain variables are
    [Show full text]
  • Parallel Solvers for Mixed Integer Linear Optimization
    Industrial and Systems Engineering Parallel Solvers for Mixed Integer Linear Optimization Ted Ralphs Lehigh University, Bethlehem, PA, USA Yuji Shinano Zuse Institute Berlin, Takustraße 7, 14195 Berlin, Germany Timo Berthold Fair Isaac Germany GmbH, Germany, Takustraße 7, 14195 Berlin, Germany Thorsten Koch Zuse Institute Berlin, Takustraße 7, 14195 Berlin, Germany COR@L Technical Report 16T-014-R3 Parallel Solvers for Mixed Integer Linear Optimization Ted Ralphs∗1, Yuji Shinanoy2, Timo Bertholdz3, and Thorsten Kochx4 1Lehigh University, Bethlehem, PA, USA 2Zuse Institute Berlin, Takustraße 7, 14195 Berlin, Germany 3Fair Isaac Germany GmbH, Germany, Takustraße 7, 14195 Berlin, Germany 4Zuse Institute Berlin, Takustraße 7, 14195 Berlin, Germany Original Publication: December 30, 2016 Last Revised: May 4, 2017 Abstract In this article, we provide an overview of the current state of the art with respect to solution of mixed integer linear optimization problems (MILPS) in parallel. Sequential algorithms for solving MILPs have improved substantially in the last two decades and commercial MILP solvers are now considered effective off-the-shelf tools for optimization. Although concerted development of parallel MILP solvers has been underway since the 1990s, the impact of improvements in sequential solution algorithms has been much greater than that which came from the application of parallel computing technologies. As a result, parallelization efforts have met with only relatively modest success. In addition, improvements to the underlying sequential solution technologies have actually been somewhat detrinental with respect to the goal of creating scalable parallel algorithms. This has made efforts at parallelization an even greater challenge in recent years. With the pervasiveness of multi-core CPUs, current state-of-the-art MILP solvers have now all been parallelized and research on parallelization is once again gaining traction.
    [Show full text]
  • Algorithmic Innovations and Software for the Dual Decomposition Method Applied to Stochastic Mixed-Integer Programs
    Noname manuscript No. (will be inserted by the editor) Algorithmic innovations and software for the dual decomposition method applied to stochastic mixed-integer programs Kibaek Kim · Victor M. Zavala Submitted: September 29, 2017 Abstract We present algorithmic innovations for the dual decomposition method to address two-stage stochastic programs with mixed-integer recourse and provide an open-source software implementation that we call DSP. Our innovations include the incorporation of Benders-like cuts in a dual decom- position framework to tighten Lagrangian subproblems and aid the exclusion of infeasible first-stage solutions for problems without (relative) complete re- course. We also use an interior-point cutting-plane method with new termi- nation criteria for solving the Lagrangian master problem. We prove that the algorithm converges to an optimal solution of the Lagrangian dual problem in a finite number of iterations, and we also prove that convergence can be achieved even if the master problem is solved suboptimally. DSP can solve in- stances specified in C code, SMPS files, and Julia script. DSP also implements a standard Benders decomposition method and a dual decomposition method based on subgradient dual updates that we use to perform benchmarks. We present extensive numerical results using SIPLIB instances and a large unit This material is based upon work supported by the U.S. Department of Energy, Office of Science, under contract number DE-AC02-06CH11357. V. M. Zavala acknowledges funding from the early career program of the U.S. Department of Energy under grant DE-SC0014114. We gratefully acknowledge the computing resources provided on Blues, a high-performance computing cluster operated by the Laboratory Computing Resource Center at Argonne National Laboratory.
    [Show full text]
  • A Generative Deep Learning Approach Towards Decisions in Transient Gas Networks
    Takustr. 7 Zuse Institute Berlin 14195 Berlin Germany LOVIS ANDERSON1,MARK TURNER2,THORSTEN KOCH3 A generative deep learning approach towards decisions in transient gas networks 1 0000-0002-4316-1862 2 0000-0001-7270-1496 3 0000-0002-1967-0077 ZIB Report Get-Later ( June? 2020) Zuse Institute Berlin Takustr. 7 14195 Berlin Germany Telephone: +49 30-84185-0 Telefax: +49 30-84185-125 E-mail: [email protected] URL: http://www.zib.de ZIB-Report (Print) ISSN 1438-0064 ZIB-Report (Internet) ISSN 2192-7782 A generative deep learning approach towards decision variables in transient gas networks Lovis Anderson, Mark Turner, Thorsten Koch January 4, 2021 Abstract A decision support system relies on frequent re-solving of similar prob- lems. These problems arise from the cyclical nature of such systems, and as such each problem has similar structure. We propose a generative neu- ral network design for learning integer decision variables of mixed-integer programming (MIP) formulations of these problems. We utilise a deep neural network discriminator and a MIP solver as our oracle to train our generative neural network. As a final product, we present the results of our design applied to the transient gas optimisation problem, where we produce a feasible solution in 2.5s, which can be used as a warm-start solution to decrease global optimal solution solve time by 60.5%. 1 Introduction Mixed-Integer Programming (MIP) is concerned with the modelling and solving of problems from discrete optimisation. These problems can represent real-world scenarios, where thanks to the integer variables, discrete decisions can be ap- propriately captured and modelled.
    [Show full text]
  • Technical Program
    TECHNICAL PROGRAM Wednesday, 9:00-10:30 Wednesday, 11:00-12:40 WA-01 WB-01 Wednesday, 9:00-10:30 - 1a. Europe a Wednesday, 11:00-12:40 - 1a. Europe a Opening session - Plenary I. Ljubic Planning and Operating Metropolitan Passenger Transport Networks Stream: Plenaries and Semi-Plenaries Invited session Stream: Traffic, Mobility and Passenger Transportation Chair: Bernard Fortz Invited session Chair: Oded Cats 1 - From Game Theory to Graph Theory: A Bilevel Jour- ney 1 - Frequency and Vehicle Capacity Determination using Ivana Ljubic a Dynamic Transit Assignment Model In bilevel optimization there are two decision makers, commonly de- Oded Cats noted as the leader and the follower, and decisions are made in a hier- The determination of frequencies and vehicle capacities is a crucial archical manner: the leader makes the first move, and then the follower tactical decision when planning public transport services. All meth- reacts optimally to the leader’s action. It is assumed that the leader can ods developed so far use static assignment approaches which assume anticipate the decisions of the follower, hence the leader optimization average and perfectly reliable supply conditions. The objective of this task is a nested optimization problem that takes into consideration the study is to determine frequency and vehicle capacity at the network- follower’s response. level while accounting for the impact of service variations on users In this talk we focus on new branch-and-cut (B&C) algorithms for and operator costs. To this end, we propose a simulation-based op- dealing with mixed-integer bilevel linear programs (MIBLPs).
    [Show full text]