diff --git a/public/challenge/2024/description_choco-solver__cp-sat_.txt b/public/challenge/2024/description_choco-solver__cp-sat_.txt new file mode 100644 index 000000000..7d875ae65 --- /dev/null +++ b/public/challenge/2024/description_choco-solver__cp-sat_.txt @@ -0,0 +1,6 @@ +Choco-solver is a java library for constraint satisfaction problems and constraint optimization problems. + +This experimental version of choco-solver combines SAT with CP to generate lazy clauses. +However, the code has not been stabilised. + +Main authors of this version is Charles Prud’homme (TASC, LS2N UMR 6241). \ No newline at end of file diff --git a/public/challenge/2024/description_choco-solver__cp_.txt b/public/challenge/2024/description_choco-solver__cp_.txt new file mode 100644 index 000000000..e2a189618 --- /dev/null +++ b/public/challenge/2024/description_choco-solver__cp_.txt @@ -0,0 +1,14 @@ +Choco-solver is a java library for constraint satisfaction problems and constraint optimization problems. + +The Choco-solver library contains numerous variables, constraints and search procedures, to provide wide modeling perspectives. +Most common variables are integer variables (including binary variables and views) but the distribution also includes set variables, graph variables and real variables. +The constraint library provided by Choco-solver contains many global constraints, which gives a very expressive modeling language. +The search process can also be greatly improved by various built-in search strategies (such as DomWDeg, CHS, ABS, first-fail, etc.) and some optimization procedures (LNS, fast restart, etc.). +Moreover, Choco-solver natively supports explained constraints. +Last, several useful extra features, such as a FlatZinc (the target language of MiniZinc) parser, are provided as well. + +Choco-solver is used by the academy for teaching and research and by the industry to solve real-world problems, such as program verification, smart grid management, timetabling, scheduling and routing. + +Main authors of Choco-solver are Charles Prud’homme (TASC, LS2N UMR 6241), Jean-Guillaume Fages (COSLING S.A.S.), Arthur Godet (Groupe SNCF) and Dimitri Justeau-Allaire (IRD). + +For any informations visit our website : http://choco-solver.org \ No newline at end of file diff --git a/public/challenge/2024/description_chuffed.txt b/public/challenge/2024/description_chuffed.txt new file mode 100644 index 000000000..a0adb1adf --- /dev/null +++ b/public/challenge/2024/description_chuffed.txt @@ -0,0 +1,74 @@ +Chuffed - A lazy clause solver + +Geoffrey Chu, Peter J. Stuckey, Andreas Schutt, Thorsten Ehlers, Graeme Gange, Kathryn Francis + +Data61, CSIRO, Australia + +Department of Computing and Information Systems +University of Melbourne, Australia + + +Chuffed is a state of the art lazy clause solver designed from the ground up +with lazy clause generation in mind. Lazy clause generation is a hybrid +approach to constraint solving that combines features of finite domain +propagation and Boolean satisfiability. Finite domain propagation is +instrumented to record the reasons for each propagation step. This creates an +implication graph like that built by a SAT solver, which may be used to create +efficient nogoods that record the reasons for failure. These nogoods can be +propagated efficiently using SAT unit propagation technology. The resulting +hybrid system combines some of the advantages of finite domain constraint +programming (high level model and programmable search) with some of the +advantages of SAT solvers (reduced search by nogood creation, and effective +autonomous search using variable activities). + +The FD components of Chuffed are very tightly integrated with a SAT solver. For +"small" variables (|D| <= 1000), SAT variables representing [x = v] or [x >= v] +are eagerly created at the start of the problem. Channelling constraints are +natively enforced by the variable objects in order to keep the FD solver and +the SAT solver's view of the domains fully consistent at all times. For "large" +variables (|D| > 1000), the SAT variables are lazily generated as needed. Every +propagator in Chuffed has been instrumented so that all propagation can be +explained by some combination of the literals in the SAT solver. An explanation +is of the form a_1 /\ ... /\ a_n -> d, where a_i represent domain restrictions +which are currently true, and d represents the domain change that is implied. +e.g. Suppose z >= x + y, and we have x >= 3, y >= 2. Then the propagator would +propagate x >= 5 with explanation clause x >= 3 /\ y >= 2 -> z >= 5. + +The explanations for each propagation form an implication graph. This allows us +to do three very important things. Firstly, we can derive a nogood to explain +each failure. Such nogoods often allow us to avoid a very large amount of +redundant work, thus producing search trees which are orders of magnitude +smaller. Secondly, nogoods allow us to make informed choices about +non-chronological back-jumping. When no literal from a decision level appears +in the nogood, it is indicative of the fact that the decision made at that +level was completely irrelevant to the search. Thus by back-jumping over such +decisions, we retrospectively avoid making such bad decisions, and hopefully +make good decisions instead which drive the search towards failure. Thirdly, by +analysing the conflict, we can actively gain some information about what good +decision choices are. The Variable State Independent Decaying Sum (VSIDS) +heuristic is an extremely effective search heuristic for SAT problems, and is +also extremely good for a range of CP problems. Each variables has an +associated activity, which is increased whenever the variable is involved in +the conflict. Variables with the highest activity is chosen as the decision +variable at each node. The activities are decayed to reflect the fact that the +set of important variables changes with time. + +Although Chuffed implements lazy clause generation, which is cutting edge and +rather complex, the FD parts of Chuffed are relatively simple. In fact, it is +quite minimalistic. Chuffed only supports 3 different propagator priorities. +Chuffed implements a number of global propagators (alldiff, inverse, +minimum, table, regular, mdd, cumulative, disjunctive, circuit, difference). +It also only supports two kinds of integer variables. Small integer variables +for which the domain is represented by a byte string. +And large integer variables for which the domain is represented only by its +upper and lower bound (no holes allowed). All boolean variables and boolean +constraints are handled by the builtin SAT solver. + +Great pains have been taken to make everything as simple and efficient as +possible. The solver, when run with lazy clause generation disabled, is +somewhat comparable in speed with older versions of Gecode. The overhead from +lazy clause generation ranges from negligible to perhaps around 100%. The +search reduction, however, can reach orders of magnitude on appropriate +problems. Thus lazy clause generation is an extremely important and useful +technology. The theory behind lazy clause generation is described in much +greater detail in various papers. \ No newline at end of file diff --git a/public/challenge/2024/description_cpofzn.txt b/public/challenge/2024/description_cpofzn.txt new file mode 100644 index 000000000..a02e53a9c --- /dev/null +++ b/public/challenge/2024/description_cpofzn.txt @@ -0,0 +1,3 @@ +CpoFzn is a CP Optimizer solver interface for MiniZinc. + +See https://github.com/IBMDecisionOptimization/cpofzn for more information. diff --git a/public/challenge/2024/description_gecode.txt b/public/challenge/2024/description_gecode.txt new file mode 100644 index 000000000..bced5faf7 --- /dev/null +++ b/public/challenge/2024/description_gecode.txt @@ -0,0 +1,22 @@ +The Gecode entry to the MiniZinc challenge 2024 + +This solver is based on the Gecode C++ constraint solving library +(upcoming version 6.0.0 + some modifications), and the Gecode/FlatZinc +interface. Both Gecode and Gecode/FlatZinc are available under the MIT +license from the web site http://www.gecode.org. + +The par version of this entry uses a mix of search strategies: + - the strategy specified in the model + - a dynamic strategy (based on AFC, aka dom_w_deg) with restarts + - an LNS strategy to generate solutions quickly (only for optimisation problems) + +Main authors: + Christian Schulte, + ICT, KTH - Royal Institute of Technology, Sweden + Mikael Lagerkvist, + ICT, KTH - Royal Institute of Technology, Sweden + Guido Tack, + Department of Computer Science at K.U. Leuven, Belgium + +A full list of authors as well as licensing information is available from +http://www.gecode.org/gecode-doc-latest/PageLic.html. \ No newline at end of file diff --git a/public/challenge/2024/description_gecode_dexter.txt b/public/challenge/2024/description_gecode_dexter.txt new file mode 100644 index 000000000..ce5d14506 --- /dev/null +++ b/public/challenge/2024/description_gecode_dexter.txt @@ -0,0 +1,10 @@ +Gecode/Dexter + +Gecode Authors: Christian Schulte, Guido Tack, and Mikael Z. Lagerkvist. +Gecode/Dexter Authors: Dexter Leander. + +Gecode/Dexter uses a portfolio search built into the classical constraint programming solver Gecode for MiniZinc, to solve optimisation problems. When solving satisfaction problems or when the solver is run with one thread, regular Gecode is used. As such, this solver is a variant of the Gecode solver. + +The portfolio supports up to eleven assets and was implemented as part of a master thesis. Six of these assets utilise LNS to navigate the search space, and they include: random, propagation-guided, reverse propagation-guided, objective relaxation, cost impact-guided, and static variable-relationship-guided LNS. Moreover, one asset compares all LNS assets and selects the one most fit for the problem at hand. Additionally, two assets are modifications of standard Gecode search, and one is identical. The portfolio also includes a shaving asset that helps find infeasible values for variables during the search. + +A more in-depth description of the assets of the portfolio and the portfolio itself can be found in the thesis at: https://www.diva-portal.org/smash/record.jsf?pid=diva2%3A1876143&dswid=-6428 \ No newline at end of file diff --git a/public/challenge/2024/description_izplus.txt b/public/challenge/2024/description_izplus.txt new file mode 100644 index 000000000..83f43f842 --- /dev/null +++ b/public/challenge/2024/description_izplus.txt @@ -0,0 +1,36 @@ +============================================================ + iZplus (fzn_izplus) -- iZ based solver for MiniZinc Challenge + Copyright (C) 2012-2024 NTT DATA SEKISUI SYSTEMS CORPORATION. +============================================================ + +* What is iZplus + + iZplus is a FlatZinc solver using iZ-C constraint programming +library which is developed by NTT DATA SEKISUI SYSTEMS CORPORATION. + +iZ-C provides : + * many built-in constraints + * constraint propagation mechanism + * simple search strategy + +And in iZplus, following algorithms are 'plus'ed. + * Randomized restarting in search + * Local search for optimization problem + * Local search for satisfying problem + * Variable reordering + * NG learning + +* Author + + Toshimitsu FUJIWARA + developer at NTT DATA SEKISUI SYSTEMS CORPORATION + +* Acknowledgement + + FlatZinc parser in fzn_izplus is based on 'FlatZinc parser skeleton' +written by Guido Tack. + + iZ-C is a constraint programming library developed by +NTT DATA SEKISUI SYSTEMS CORPORATION. + + http://www.constraint.org/en/izc_download.html \ No newline at end of file diff --git a/public/challenge/2024/description_jacop.txt b/public/challenge/2024/description_jacop.txt new file mode 100644 index 000000000..31b9a9c6f --- /dev/null +++ b/public/challenge/2024/description_jacop.txt @@ -0,0 +1,28 @@ +JaCoP - Java Constraint Programming Solver + +JaCoP solver is Java-based open source solver developed and maintained mainly by two people Krzysztof Kuchcinski - Dept. of Computer Science, Lund University, Sweden, and Radoslaw Szymanek - Utopix, Switzerland. + +Moreover a number of students have contributed to the solver by programming first versions of different global constraints and set constraints. The solver is being used in academia for research and teaching as well as in industry for commercial purposes. The most successful use of the solver is within Electronic Design Automation community, since both main authors come from that community. + +JaCoP supports finite domains of integers and sets of integers as well as floating-point domain. It offers a significant number of primitive and global constraints on these variables to facilitate modeling as well as modular design of search. This allows to tailor search to characteristics of the problem being addressed. It has currently more than 100,000 lines of code, not including examples and testing code. The examples which are the preferred way to document the abilities of JaCoP have more than 20.000 lines of code. The core developers have been working on JaCoP for past 10 years during their free time as a hobby activity. It has been refactored, transformed, and improved many times. Initial versions of JaCoP were even 3 orders of magnitude slower than the current version. JaCoP implementation has been influenced heavily by more than 20 research articles. Moreover, JaCoP was used as a tool to conduct experiments for CP publications. + +The major focus of JaCoP are its constraints. These constraints include rich set of primitive, logical, and conditional constraints as well as many global constraints. The most important global constraints are as follows. + +- diff2, +- cumulative, +- alldifferent (with bounds consistency and complete algorithm based + on Régin's algorithm), +- gcc, +- extensional support (with three different state-of-the-art approaches) + and extensional conflict, +- among, +- element, +- circuit and subcircuit, +- knapsack, +- binpacking, +- regular, +- netflow, and +- geost. + + +JaCoP solver can be used directly from Java but it has several front-ends. It has FlatZinc language interface that makes it possible to execute MiniZinc models. It allows us to perform extensive testing with the help of other solvers as we can compare results from different solvers. It has also Scala based domain-specific language so it is easier to create your own constraint programs even in more intuitive manner. \ No newline at end of file diff --git a/public/challenge/2024/description_mzn-cbc.txt b/public/challenge/2024/description_mzn-cbc.txt new file mode 100644 index 000000000..8f65c7236 --- /dev/null +++ b/public/challenge/2024/description_mzn-cbc.txt @@ -0,0 +1,5 @@ +MiniZinc 2.8 MILP interface for OSICBC 2.10 +MiniZinc Release 2.8.5 +http://www.minizinc.org/software.html + +Gleb Belov, Peter J. Stuckey, Guido Tack, Mark Wallace. Improved Linearization of Constraint Programming Models. In M. Rueher, editor, Principles and Practice of Constraint Programming - 22nd International Conference, CP 2016, Toulouse, France, September 5-9, 2016, Proceedings. LNCS 9892, pp. 49-65, Springer, 2016. diff --git a/public/challenge/2024/description_mzn-cplex.txt b/public/challenge/2024/description_mzn-cplex.txt new file mode 100644 index 000000000..ff1ca1274 --- /dev/null +++ b/public/challenge/2024/description_mzn-cplex.txt @@ -0,0 +1,5 @@ +MiniZinc 2.8 MILP interface for IBM ILOG CPLEX 22.11 +MiniZinc Release 2.8.5 +http://www.minizinc.org/software.html + +Gleb Belov, Peter J. Stuckey, Guido Tack, Mark Wallace. Improved Linearization of Constraint Programming Models. In M. Rueher, editor, Principles and Practice of Constraint Programming - 22nd International Conference, CP 2016, Toulouse, France, September 5-9, 2016, Proceedings. LNCS 9892, pp. 49-65, Springer, 2016. diff --git a/public/challenge/2024/description_mzn-gurobi.txt b/public/challenge/2024/description_mzn-gurobi.txt new file mode 100644 index 000000000..a2323f30c --- /dev/null +++ b/public/challenge/2024/description_mzn-gurobi.txt @@ -0,0 +1,5 @@ +MiniZinc 2.8 MILP interface for Gurobi 11.0.3 +MiniZinc Release 2.8.5 +http://www.minizinc.org/software.html + +Gleb Belov, Peter J. Stuckey, Guido Tack, Mark Wallace. Improved Linearization of Constraint Programming Models. In M. Rueher, editor, Principles and Practice of Constraint Programming - 22nd International Conference, CP 2016, Toulouse, France, September 5-9, 2016, Proceedings. LNCS 9892, pp. 49-65, Springer, 2016. diff --git a/public/challenge/2024/description_mzn-highs.txt b/public/challenge/2024/description_mzn-highs.txt new file mode 100644 index 000000000..3b69a4e77 --- /dev/null +++ b/public/challenge/2024/description_mzn-highs.txt @@ -0,0 +1,5 @@ +MiniZinc 2.8 MILP interface for HiGHS 1.7.2 +MiniZinc Release 2.8.5 +http://www.minizinc.org/software.html + +Gleb Belov, Peter J. Stuckey, Guido Tack, Mark Wallace. Improved Linearization of Constraint Programming Models. In M. Rueher, editor, Principles and Practice of Constraint Programming - 22nd International Conference, CP 2016, Toulouse, France, September 5-9, 2016, Proceedings. LNCS 9892, pp. 49-65, Springer, 2016. diff --git a/public/challenge/2024/description_mzn-scip.txt b/public/challenge/2024/description_mzn-scip.txt new file mode 100644 index 000000000..6c9aa9fd7 --- /dev/null +++ b/public/challenge/2024/description_mzn-scip.txt @@ -0,0 +1,5 @@ +MiniZinc 2.8 MILP interface for SCIP 9.1.0 +MiniZinc Release 2.8.5 +http://www.minizinc.org/software.html + +Gleb Belov, Peter J. Stuckey, Guido Tack, Mark Wallace. Improved Linearization of Constraint Programming Models. In M. Rueher, editor, Principles and Practice of Constraint Programming - 22nd International Conference, CP 2016, Toulouse, France, September 5-9, 2016, Proceedings. LNCS 9892, pp. 49-65, Springer, 2016. diff --git a/public/challenge/2024/description_optimathsat.txt b/public/challenge/2024/description_optimathsat.txt new file mode 100644 index 000000000..7742b74b5 --- /dev/null +++ b/public/challenge/2024/description_optimathsat.txt @@ -0,0 +1,187 @@ +=============================================================================== + + NOTES + +=============================================================================== + +OptiMathSAT-INT: problems are converted from FlatZinc to in OMT(QF_LIRA), where +QF_LIRA stands for the Theory of Linear Arithmetic, and solved with OptiMathSAT. + +OptiMathSAT-INT-SMT2: problems are converted from FlatZinc to in OMT(QF_LIRA), +where QF_LIRA stands for the Theory of Linear Arithmetic, and solved with +OptiMathSAT. This configuration uses custom global constraints for SMT2. + +OptiMathSAT-BV: problems are converted from FlatZinc to in OMT(QF_BV), where +QF_BV stands for the Theory of BitVectors, and solved with OptiMathSAT. + +Past experimental evidence suggests that QF_LIRA and QF_BV have complementary +advantages and tend to outperform one another on different problems. + +Below follows a description of the tools included in this submission. + +=============================================================================== + + fzn2omt + + https://github.com/PatrickTrentin88/fzn2omt + +=============================================================================== + +The fzn2omt library is a collection of python scripts to solve FlatZinc models +with Optimization Modulo Theories solvers like, e.g., Barcelogic, OptiMathSAT +and z3. + +Satisfaction models can also be solved with Satisfiability Modulo Theory solvers +like, e.g., CVC4. + +All tools support the encoding based on the QF_LIRA logic (option --int-enc), +that may sometimes be extended to QF_NIRA when the input problem requires it. +OptiMathSAT, z3 and CVC4 support also the encoding based on the QF_BV logic +(option --bv-enc). + +Both OptiMathSAT and z3 support the following multi-objective combinations +over FlatZinc models: + +- Multi-Independent optimization (box): each objective is considered an + independent optimization goal from the others, so there is one optimal + solution for each goal; +- Lexicographic optimization (lex): the objectives are optimized in decreasing + order of priority, so there is only one optimal solution; +- Pareto optimization (par): all Pareto-optimal solutions are returned; +A FlatZinc model with multiple objectives may look like: + +``` +solve minimize cost, maximize profit; +``` + +To know how to select the desired multi-objective combination with OptiMathSAT +and z3, look at the examples in the official documentation. + +Barcelogic does not have native support for multi-objective optimization. Thus, +multiple optimization targets are solved incrementally. + +SMT/OMT solvers use infinite-precision arithmetic. Upon user request, models are +printed with finite precision by default. It is possible to print a model with +infinite precision using the option --infinite-precision. + +Authors: + Patrick Trentin + (Former PhD Student/Post-Doc, University of Trento - DISI, Italy) + + +=============================================================================== + + OptiMathSAT + + http://optimathsat.disi.unitn.it/ + +=============================================================================== + +OptiMathSAT is a state-of-the-art Optimization Modulo Theories (OMT) solver +based on the SMT solver MathSAT5 (http://mathsat.fbk.eu/). OptiMathSAT features +both single- and multi-objective optimization over arbitrary sets of Linear +Rational Arithmetic (LRA), Linear Integer Arithmetic (LIA), Linear Integer and +Rational Arithmetic (LIRA), Bit-Vectors (BV), Floating-Point (FP), +Pseudo-Boolean (PB) and MaxSMT cost functions. Multiple objective functions +can be combined with one another into a Lexicographic or a Pareto optimization +problem, or independently solved in a single run (for the best efficiency). + +Satisfiability Modulo Theories (SMT) is the problem of deciding the +satisfiability of a first-order formula with respect to a combination of +decidable first-order theories. Typical theories of SMT interest are (the +theory of) linear arithmetic over the rationals (LRA), the integers (LIA) +or their combination (LIRA), non-linear arithmetic over the rationals (NLRA) +or the integers (NLIA), arrays (AR), bit-vectors (BV), floating-point +arithmetic (FP), and their combination thereof. The last two decades have +witnessed the development of very efficient SMT solvers based on the so-called +lazy-SMT schema. + +Optimization Modulo Theories (OMT) is an extension to Satisfiability Modulo +Theories (SMT) that allows for finding a model of a first-order formula that +is optimal with respect to some objective function expressed in some background +theory, by means of a combination of SMT and optimization procedures. + + + +Distinctive Features: + +1. Multi-Objective optimization + + + OptiMathSAT extends the FlatZinc with Multi-Objective optimization, e.g. + + solve minimize cost, maximize revenue; + + Multiple objectives are combined together using the strategy specified by + the option '-opt.priority=[box|lex|par]', that is: + + - par: performs a classic Pareto-front exploration + - lex: optimizes the objectives in lexicographic order + - box: optimizes each objective independently from the others + +2. Infinite-Precision Arithmetic: every arithmetical operation is performed + with an infinite precision arithmetic library (e.g. GMP) to ensure the + correctness of the result with absolute precision. Unbounded variables are + assumed to have an infinite domain ]-INF, +INF[. + + +3. OptiMathSAT can be used as a FZN2OMT interface to other OMT solvers like + Z3 (https://github.com/Z3Prover/z3) and Barcelogic (https://barcelogic.com/). + To discover more about this, see https://github.com/PatrickTrentin88/fzn2omt . + + + +The OptiMathSAT team is comprised by: + + - Roberto Sebastiani, founder, from 2009 to present + (Associate Professor at University of Trento - DISI, Italy) + - Silvia Tomasi, founder, from 2009 to 2014 + (Former PhD Student, University of Trento - DISI, Italy) + - Patrick Trentin, from June 2013 to June 2020 + (Former PhD Student/Post-Doc, University of Trento - DISI, Italy) + + + +Publications: + + [cpaior_cts20] + F. Contaldo, P. Trentin and R. Sebastiani. + From MiniZinc to Optimization Modulo Theories, and Back. + CPAIOR 2020 (To Appear) + + [cade_st19] + P. Trentin and R. Sebastiani. + Optimization Modulo the Theory of Floating-Point Numbers. + Automated Deduction, CADE 27, 2019. + (https://doi.org/10.1007/978-3-030-29436-6_33) + + [st_jar18] + R. Sebastiani and P. Trentin. + OptiMathSAT: A Tool for Optimization Modulo Theories. + Journal of Automated Reasoning, December 2018. + (https://doi.org/10.1007/s10817-018-09508-6) + + [st_tacas17] + R. Sebastiani and P. Trentin. + On Optimization Modulo Theories, MaxSMT and Sorting Networks. + In Proc. Int. Converence on Tools and Algorithms for the Construction and Analysis of Systems, TACAS 2017. + + [st_cav15] + R. Sebastiani and P. Trentin. + OptiMathSAT: A Tool for Optimization Modulo Theories. + In Proc. International Converence on Computer-Aided Verification, CAV 2015. + + [st_tacas15] + R. Sebastiani and P. Trentin. + Pushing the Envelope of Optimization Modulo Theories with Linear-Arithmetic Cost Functions. + TACAS, 2015. + + [st_tocl14] R. Sebastiani and S. Tomasi. + Optimization Modulo Theories with Linear Rational Costs. + ACM Transactions on Computational Logic, March 2015. + + [st-ijcar12] + R. Sebastiani and S. Tomasi. + Optimization in SMT with LA(Q) Cost Functions. + IJCAR, July 2012. + (http://dx.doi.org/10.1007/978-3-642-31365-3_38) \ No newline at end of file diff --git a/public/challenge/2024/description_or-tools_cp-sat.txt b/public/challenge/2024/description_or-tools_cp-sat.txt new file mode 100644 index 000000000..e81a714d6 --- /dev/null +++ b/public/challenge/2024/description_or-tools_cp-sat.txt @@ -0,0 +1,27 @@ +CP-SAT is a discrete optimization solver built on top of a SAT engine. + +It is available within the OR-Tools open-source repository + - website: https://developers.google.com/optimization + - github repository: https://github.com/google/or-Tools +It has won multiple gold medals at the MiniZinc challenge: + https://www.minizinc.org/challenge.html since its debut in 2017. + +The CP-SAT solver is architectured around five components: + - The base layer is a clause learning SAT solver. + - Above the SAT layer sits a Constraint Programming (CP) module with Boolean, + integer and interval variables, and standard integer, scheduling and routing + constraints. + - Alongside the CP solver, a simplex provides a global linear relaxation. + Its integration with the CP and SAT layers enable the CP-SAT solver to solve + MIP problems with the same techniques as (commercial) MIP solvers: + relaxation, cuts, heuristics and duality based techniques. + - Both the CP and MIP modules rely on a unified protobuf representation of the + model that can serve as a file format, as well as an intermediate + representation of the model during all phases of the solve (input format, + presolved model, LNS fragment, Local Search). + - On top, the search layer implements a robust information-sharing portfolio + of specialized workers that offers both good and fast solutions, and + superior optimality proving capabilities. + +This work was presented at the CPAIOR 2020 masterclass. The recording is +available on youtube (https://www.youtube.com/watch?v=lmy1ddn4cyw). \ No newline at end of file diff --git a/public/challenge/2024/description_or-tools_cp-sat_ls.txt b/public/challenge/2024/description_or-tools_cp-sat_ls.txt new file mode 100644 index 000000000..cf3ab410a --- /dev/null +++ b/public/challenge/2024/description_or-tools_cp-sat_ls.txt @@ -0,0 +1,24 @@ +CP-SAT-LS is a discrete optimization solver built on top of a SAT engine. + +It is available within the OR-Tools open-source repository + - website: https://developers.google.com/optimization + - github repository: https://github.com/google/or-Tools +It has won multiple gold medals at the MiniZinc challenge: + https://www.minizinc.org/challenge.html since its debut in 2017. + +The CP-SAT-LS solver is a pure Local Search solver using the same interface as +the CP-SAT solver. + +It is build on top of the Feasibility Jump article: + Luteberget, B., Sartor, G. + Feasibility Jump: an LP-free Lagrangian MIP heuristic. + Math. Prog. Comp. 15, 365–388 (2023). + https://doi.org/10.1007/s12532-023-00234-8 + +Complemented by the ViolationLS article: + Davies, T.O., Didier, F., Perron, L. (2024). + ViolationLS: Constraint-Based Local Search in CP-SAT. + Dilkina, B. (eds) Integration of Constraint Programming, Artificial + Intelligence, and Operations Research. CPAIOR 2024. + Lecture Notes in Computer Science, vol 14742. Springer, Cham. + https://doi.org/10.1007/978-3-031-60597-0_16 \ No newline at end of file diff --git a/public/challenge/2024/description_picatsat.txt b/public/challenge/2024/description_picatsat.txt new file mode 100644 index 000000000..1c87eabe1 --- /dev/null +++ b/public/challenge/2024/description_picatsat.txt @@ -0,0 +1,49 @@ +PicatSAT: A SAT Compiler in Picat + + Neng-Fa Zhou and Hakan Kjellerstrand + + +Picat is a simple, and yet powerful, logic-based multi-paradigm +programming language aimed for general-purpose applications. Picat is +a rule-based language, in which predicates, functions, and actors are +defined with pattern-matching rules. Picat incorporates many +declarative language features for better productivity of software +development, including explicit non-determinism, explicit +unification, functions, list comprehensions, constraints, and +tabling. Picat also provides imperative language constructs, such as +assignments and loops, for programming everyday things. The Picat +implementation, which is based on a well-designed virtual machine and +incorporates a memory manager that garbage-collects and expands the +stacks and data areas when needed, is efficient and scalable. Picat +can be used for not only symbolic computations, which is a +traditional application domain of declarative languages, but also for +scripting and modeling tasks. + +Picat provides facilities for solving combinatorial search problems, +including a common interface with CP, SAT, and MIP solvers, tabling +for dynamic programming, and a module for planning. The common +interface allows for seamless switching from one solver module to +another. + +For the sat module, the Picat compiler translates constraints into a +logic formula in the conjunctive normal form (CNF) for the underlying +SAT solver (the Maple solver is used for the competition). Picat +employs the so called log-encoding for compiling domain variables and +constraints. For a domain with the maximum absolute value n, +log_2(n) Boolean variables are used. If the domain contains both +negative and positive values, then another Boolean variable is used to +encode the sign. Each combination of values of these Boolean variables +represents a valuation for the domain variable. If there are holes in +the domain, then disequality (!=) constraints are generated in order +to disallow assignments of those hole values to the variable. Picat-SAT +flattens intensional constraints into primitive ones. It performs +numerous optimizations, both in the phase of breaking constraints into +primitive ones and in the phase of compiling primitive constraints +into adders and comparators. Details can be found in: + +N.F. Zhou and H. Kjellerstrand: +Optimizing SAT Encodings for Arithmetic Constraints, +CP 2017 + +We would like to thank Joachim Schimpf for sharing his FlatZinc parser +with us and Sanders Hernandez for implementing the Maple interface. \ No newline at end of file diff --git a/public/challenge/2024/description_pumpkin.txt b/public/challenge/2024/description_pumpkin.txt new file mode 100644 index 000000000..8515572ae --- /dev/null +++ b/public/challenge/2024/description_pumpkin.txt @@ -0,0 +1,16 @@ +Pumpkin is a new combinatorial optimisation solver that is under active development at the ConSol Lab at TU Delft. +The contributors (all at TU Delft) of Pumpkin are (in alphabetical order): Emir Demirović, Maarten Flippo, Imko Marijnissen, Konstantin Sidorov, and Jeff Smits. + +Pumpkin has a FlatZinc frontend, but can also be used to solve SAT and MaxSAT problems. Support for CPMpy and XCSP is planned for the near future. +The solver is written in pure Rust and also exposes a Rust API which allows users to express models with predefined constraints and implement custom branching strategies. + +The solver is based on the (lazy clause generation) constraint programming paradigm. +It supports integer variables, the FlatZinc built-in constraints for integer and boolean variables, and the cumulative global constraint. + +A unique feature of Pumpkin is that it can produce a certificate of unsatisfiability as part of the solving process without significant overhead. See our CP’24 [1] paper for more details. + +The solver is reasonably competitive, although depending on the problem, other state-of-the-art solvers may be better. We are planning many improvements for the near future. + +For more information, the repository can be found at https://github.com/ConSol-Lab/pumpkin. We welcome questions in the Discussion section of GitHub. + +[1]. A Multi-Stage Proof Logging Framework to Certify the Correctness of CP Solvers. Flippo, Sidorov, Marijnissen, Smits, Demirović. CP'24. \ No newline at end of file diff --git a/public/challenge/2024/description_sicstus_prolog.txt b/public/challenge/2024/description_sicstus_prolog.txt new file mode 100644 index 000000000..4d402fb9e --- /dev/null +++ b/public/challenge/2024/description_sicstus_prolog.txt @@ -0,0 +1,65 @@ +SICStus Prolog, http://sicstus.sics.se + +Authors: Mats Carlsson et al., RISE, Sweden + +Prolog development environment with a finite domain constraint programming module, global constraints, indexicals, MiniZinc/FlatZinc interface + +APIs: MiniZinc, FlatZinc, Prolog, C++, C, C#, Java, Tcl + +Global constraints: + (arithmetical), + (Boolean), + all_different/[1,2], + all_different_except_0/1, + all_distinct/[1,2], + all_distinct_except_0/1, + all_equal/1, + all_equal_reif/2, + assignment/[2,3], + automaton/[3,8,9], + bin_packing/2, + bool_and/2, + bool_channel/4, + bool_or/2, + bool_xor/2, + case/[3,4], + circuit/[1,2], + count/4, + cumulative/[1,2], + cumulatives/[2,3], + decreasing/[1,2], + diffn/[1,2], + disjoint1/[1,2], + disjoint2/[1,2], + element/[2,3], + geost/[2,3,4], + global_cardinality/[2,3], + increasing/[1,2], + keysorting/[2,3], + lex_chain/[1,2], + maximum/2, + maximum_arg/2, + minimum/2, + minimum_arg/2, + multi_cumulative/[2,3], + network_flow/[2,3], + nvalue/2, + regular/2, + relation/3, + scalar_product/[4,5], + scalar_product_reif/[5,6], + seq_precede_chain/[1,2], + smt/1, + sorting/3, + subcircuit/[1,2], + sum/3, + symmetric_all_different/1, + symmetric_all_distinct/1, + table/[2,3], + value_precede_chain/[2,3]. + +Standards: ISO Prolog standard, IPv4, IPv6, Unicode 5.0 + +Since: 1995 + +Current release: SICStus Prolog 4.9.0, Dec 18, 2023 \ No newline at end of file diff --git a/public/challenge/2024/description_yuck.txt b/public/challenge/2024/description_yuck.txt new file mode 100644 index 000000000..3cb159318 --- /dev/null +++ b/public/challenge/2024/description_yuck.txt @@ -0,0 +1,12 @@ +Yuck is a FlatZinc interpreter that integrates with the MiniZinc +toolchain. It combines local search with restarting, global +constraints, and lexicographic cost functions. + +Yuck is written in Scala and runs on any system with a Java 8 (or +higher) runtime environment. + +Yuck is open source and provided under the terms of the Mozilla Public +License 2.0. + +For source code, binary packages, and documentation, please see +https://github.com/informarte/yuck. \ No newline at end of file diff --git a/public/challenge/2024/mznc2024_probs.tar.gz b/public/challenge/2024/mznc2024_probs.tar.gz new file mode 100644 index 000000000..c3334d1f4 Binary files /dev/null and b/public/challenge/2024/mznc2024_probs.tar.gz differ diff --git a/public/challenge/2024/results.js b/public/challenge/2024/results.js new file mode 100644 index 000000000..d1337b50e --- /dev/null +++ b/public/challenge/2024/results.js @@ -0,0 +1,6 @@ +const json = {"results": {"nb_s": 41, "nb_p": 20, "solvers": ["cbc-free", "cbc-par", "choco-solver__cp_-fd", "choco-solver__cp_-free", 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"free_solvers": [true, false, false, true, false, false, true, false, true, true, false, true, false, true, false, false, true, false, true, false, true, false, false, true, true, true, true, false, true, false, true, false, true, false, true, true, false, false, true, true, false], "par_solvers": [false, true, false, false, true, false, true, false, true, false, true, false, true, false, true, false, false, true, false, true, false, true, false, true, true, true, true, false, false, true, false, true, true, false, true, false, true, false, true, false, true], "open_solvers": [false, true, false, false, true, false, true, false, true, false, true, false, true, false, true, true, false, true, false, true, false, true, false, true, true, true, true, false, false, true, false, true, true, false, true, false, true, false, true, false, true], "local_solvers": [false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, 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"mznc2024_probs//tiny-cvrp/TinyCVRP_ExactlyOneSolution.mzn", "mznc2024_probs//train-scheduling/trains.mzn", "mznc2024_probs//triangular/triangular.mzn", null, "mznc2024_probs//yumi-dynamic/yumi-dynamic.mzn"], "instances": ["mznc2024_probs//accap/accap_a10_f80_t50.json", "mznc2024_probs//accap/accap_a20_f160_t90.json", "mznc2024_probs//accap/accap_a30_f300_t120.json", "mznc2024_probs//accap/accap_a3_f20_t10.json", "mznc2024_probs//accap/accap_a8_f50_t30.json", "mznc2024_probs//aircraft-disassembly/B737NG-600-01-Anon.json.dzn", "mznc2024_probs//aircraft-disassembly/B737NG-600-02-Anon.json.dzn", "mznc2024_probs//aircraft-disassembly/B737NG-600-04-Anon.json.dzn", "mznc2024_probs//aircraft-disassembly/B737NG-600-06-Anon.json.dzn", "mznc2024_probs//aircraft-disassembly/B737NG-600-09-Anon.json.dzn", "mznc2024_probs//cable-tree-wiring/A022.dzn", "mznc2024_probs//cable-tree-wiring/A033.dzn", "mznc2024_probs//cable-tree-wiring/A041.dzn", "mznc2024_probs//cable-tree-wiring/A046.dzn", "mznc2024_probs//cable-tree-wiring/R193.dzn", "mznc2024_probs//community-detection/Polblogs.s2480.k2.dzn", "mznc2024_probs//community-detection/Zakhary.s12.k3.dzn", "mznc2024_probs//community-detection/Zakhary.s12.k4.dzn", "mznc2024_probs//community-detection/adjourn.s200.k3.dzn", "mznc2024_probs//community-detection/mexican.s26.k4.dzn", "mznc2024_probs//compression/bin_10.json", "mznc2024_probs//compression/bin_20.json", "mznc2024_probs//compression/bin_7.json", "mznc2024_probs//compression/bin_8.json", "mznc2024_probs//compression/lipsum_10.json", "mznc2024_probs//concert-hall-cap/concert-cap.mznc2018.04.dzn", "mznc2024_probs//concert-hall-cap/concert_100_20.json", "mznc2024_probs//concert-hall-cap/concert_150_20.json", "mznc2024_probs//concert-hall-cap/concert_200_20.json", "mznc2024_probs//concert-hall-cap/concert_60_50.json", "mznc2024_probs//fox-geese-corn/fgc_06_26_08_00.dzn", "mznc2024_probs//fox-geese-corn/fgc_20_20_22_00.dzn", "mznc2024_probs//fox-geese-corn/foxgeesecorn_17.dzn", "mznc2024_probs//fox-geese-corn/foxgeesecorn_19.dzn", "mznc2024_probs//fox-geese-corn/foxgeesecorn_61.dzn", "mznc2024_probs//graph-clear/planar_n20_seed2022_14.json", "mznc2024_probs//graph-clear/planar_n20_seed2022_15.json", "mznc2024_probs//graph-clear/planar_n20_seed2022_16.json", "mznc2024_probs//graph-clear/planar_n30_seed2022_4.json", "mznc2024_probs//graph-clear/planar_n30_seed2022_8.json", "mznc2024_probs//harmony/brother.dzn", "mznc2024_probs//harmony/canon.dzn", "mznc2024_probs//harmony/minuet.dzn", "mznc2024_probs//harmony/ode.dzn", "mznc2024_probs//harmony/twinkle.dzn", "mznc2024_probs//hoist-benchmark/PU_2_2_3.dzn", "mznc2024_probs//hoist-benchmark/PU_2_4_3.dzn", "mznc2024_probs//hoist-benchmark/PU_2_8_4.dzn", "mznc2024_probs//hoist-benchmark/PU_3_2_1.dzn", "mznc2024_probs//hoist-benchmark/PU_3_4_3.dzn", "mznc2024_probs//monitor-placement-1id/hop_counting_based_caida_139203.dzn", "mznc2024_probs//monitor-placement-1id/hop_counting_based_zoo_Forthnet.dzn", "mznc2024_probs//monitor-placement-1id/igp_weight_based_defo_rf6461_real_hard.dzn", "mznc2024_probs//monitor-placement-1id/igp_weight_based_rocketfuel_rf1755_real_hard.dzn", "mznc2024_probs//monitor-placement-1id/igp_weight_based_rocketfuel_rf6461_real_hard.dzn", "mznc2024_probs//neighbours/neightbours-new-15.dzn", "mznc2024_probs//neighbours/neightbours-new-2.dzn", "mznc2024_probs//neighbours/neightbours-new-5.dzn", "mznc2024_probs//neighbours/neightbours-new-6.dzn", "mznc2024_probs//neighbours/neightbours-new-7.dzn", "mznc2024_probs//network_50_cstr/MODEL1507180015.dzn", "mznc2024_probs//network_50_cstr/MODEL1507180022.dzn", "mznc2024_probs//network_50_cstr/MODEL2204150001.dzn", "mznc2024_probs//network_50_cstr/MODEL2204190004.dzn", "mznc2024_probs//network_50_cstr/toy_network_1000_m.dzn", "mznc2024_probs//peacable_queens/pq_n12.dzn", "mznc2024_probs//peacable_queens/pq_n19.dzn", "mznc2024_probs//peacable_queens/pq_n35.dzn", "mznc2024_probs//peacable_queens/pq_n7.dzn", "mznc2024_probs//peacable_queens/pq_n9.dzn", "mznc2024_probs//portal/random_10_0_100.json", "mznc2024_probs//portal/random_10_9_10.json", "mznc2024_probs//portal/random_20_1_100.json", "mznc2024_probs//portal/random_20_9_100.json", "mznc2024_probs//portal/small_3.dzn", "mznc2024_probs//tiny-cvrp/easy_instance_04.dzn", "mznc2024_probs//tiny-cvrp/hard_instance_18.dzn", "mznc2024_probs//tiny-cvrp/medium_instance_06.dzn", "mznc2024_probs//tiny-cvrp/medium_instance_11.dzn", "mznc2024_probs//tiny-cvrp/medium_instance_13.dzn", "mznc2024_probs//train-scheduling/trains05.dzn", "mznc2024_probs//train-scheduling/trains09.dzn", "mznc2024_probs//train-scheduling/trains12.dzn", "mznc2024_probs//train-scheduling/trains15.dzn", "mznc2024_probs//train-scheduling/trains18.dzn", "mznc2024_probs//triangular/n11.dzn", "mznc2024_probs//triangular/n14.dzn", "mznc2024_probs//triangular/n20.dzn", "mznc2024_probs//triangular/n31.dzn", "mznc2024_probs//triangular/n9.dzn", "mznc2024_probs//word-equations/word_equations_01_track_123-int.mzn", "mznc2024_probs//word-equations/word_equations_01_track_140-int.mzn", "mznc2024_probs//word-equations/word_equations_02_track_8-int.mzn", "mznc2024_probs//word-equations/word_equations_03_track_133-int.mzn", "mznc2024_probs//word-equations/word_equations_03_track_31-int.mzn", "mznc2024_probs//yumi-dynamic/p_4_GG_GG_yumi_grid_setup_5_5_zones.dzn", "mznc2024_probs//yumi-dynamic/p_5_GG_GGG_yumi_grid_setup_5_5_zones.dzn", "mznc2024_probs//yumi-dynamic/p_5_GSS_SG_yumi_grid_setup_3_4_zones.dzn", "mznc2024_probs//yumi-dynamic/p_7_SSSS_SSS_yumi_grid_setup_3_3_zones.dzn", "mznc2024_probs//yumi-dynamic/p_9_GSGS_GSGSG_yumi_grid_setup_7_7_zones.dzn"]}}; +for (const k in json.results) { + window[k] = json.results[k]; +} +var loc_problems = json.locations.problems; +var loc_instances = json.locations.instances; diff --git a/src/components/AnnouncementBanner.jsx b/src/components/AnnouncementBanner.jsx index 9df7b74fa..151891c98 100644 --- a/src/components/AnnouncementBanner.jsx +++ b/src/components/AnnouncementBanner.jsx @@ -2,27 +2,12 @@ import { Container } from '@/components/Container' import { faAward } from '@fortawesome/free-solid-svg-icons' import { FontAwesomeIcon } from '@fortawesome/react-fontawesome' import { Link } from '@/components/Link' +import { resultsAnnouncement } from '@/data/challenge/banners' import clsx from 'clsx' export function AnnouncementBanner({ className, ...props }) { - const announcement = { - icon: faAward, - className: 'bg-orange-500', - contents: ( - - MiniZinc has been announced as a finalist for the{' '} - - 2024 Eureka Prize for Excellence in Research Software - - ! - - ), - } + const announcement = resultsAnnouncement return (
+ 2024-09-05 Announcements of the results are{' '} + here. + , <> 2024-07-15 Update the{' '} diff --git a/src/data/challenge/medals.js b/src/data/challenge/medals.js index e20eb8098..14bc7d1ab 100644 --- a/src/data/challenge/medals.js +++ b/src/data/challenge/medals.js @@ -93,4 +93,11 @@ export const medals = { par: ['OR-Tools', 'PicatSAT', 'Choco 4'], local: ['Yuck'], }, + 2024: { + fd: ['OR-Tools CP-SAT', 'Choco-solver CP-SAT', 'SICStus Prolog'], + free: ['OR-Tools CP-SAT', 'PicatSAT', 'iZplus'], + par: ['OR-Tools CP-SAT', 'PicatSAT', 'Choco-solver CP'], + open: ['OR-Tools CP-SAT', 'PicatSAT', 'Choco-solver CP'], + local: ['OR-Tools CP-SAT LS', 'Yuck'], + }, } diff --git a/src/data/challenge/problems.js b/src/data/challenge/problems.js index a91957687..b688d9c8a 100644 --- a/src/data/challenge/problems.js +++ b/src/data/challenge/problems.js @@ -1238,6 +1238,76 @@ export const probs = { sbc: true, globals: ['lex_greatereq', 'lex_less'], }, + 'aircraft-disassembly': { + type: 'real', + globals: ['cumulative'], + kind: 'min', + sbc: false, + rc: false, + }, + compression: { + type: 'combi', + globals: ['global_cardinality'], + kind: 'min', + sbc: false, + rc: false, + }, + 'graph-clear': { + type: 'combi', + globals: ['all_different'], + kind: 'min', + sbc: false, + rc: false, + }, + harmony: { + type: 'puzzle', + globals: ['all_equal', 'count_eq', 'min', 'strictly_decreasing'], + kind: 'min', + sbc: false, + rc: false, + }, + 'monitor-placement-1id': { + type: 'real', + globals: [], + kind: 'min', + sbc: false, + rc: false, + }, + network_50_cstr: { + type: 'real', + globals: [], + kind: 'min', + sbc: false, + rc: false, + }, + portal: { + type: 'puzzle', + globals: [], + kind: 'min', + sbc: false, + rc: false, + }, + 'tiny-cvrp': { + type: 'real', + globals: [], + kind: 'min', + sbc: false, + rc: false, + }, + 'train-scheduling': { + type: 'real', + globals: ['all_different', 'cumulative', 'disjunctive'], + kind: 'min', + sbc: false, + rc: false, + }, + 'word-equations': { + type: 'combi', + globals: ['nvalue'], + kind: 'max', + sbc: false, + rc: false, + }, } export const years = { @@ -1564,4 +1634,26 @@ export const years = { 'vrplc', 'yumi-static', ], + 2024: [ + 'accap', + 'aircraft-disassembly', + 'cable-tree-wiring', + 'community-detection', + 'compression', + 'concert-hall-cap', + 'fox-geese-corn', + 'graph-clear', + 'harmony', + 'hoist-benchmark', + 'monitor-placement-1id', + 'neighbours', + 'network_50_cstr', + 'peacable_queens', + 'portal', + 'tiny-cvrp', + 'train-scheduling', + 'triangular', + 'word-equations', + 'yumi-dynamic', + ], } diff --git a/src/pages/challenge/2024/results.jsx b/src/pages/challenge/2024/results.jsx new file mode 100644 index 000000000..b9346a009 --- /dev/null +++ b/src/pages/challenge/2024/results.jsx @@ -0,0 +1,215 @@ +import { Container } from '@/components/Container' +import { Footer } from '@/components/Footer' +import { Header } from '@/components/Header' +import { Breadcrumbs } from '@/components/Breadcrumbs' +import { Globals } from '@/components/challenge/Globals' +import { Link } from '@/components/Link' +import { Results } from '@/components/challenge/Results' +import { info } from '@/data/challenge/info' +import Head from 'next/head' + +export default function ResultsPage() { + return ( + <> + + MiniZinc - Challenge 2024 Results + +
+
+ +
+

MiniZinc Challenge 2024 Results

+ + + +

Entrants

+ +

+ The entrants for this year (with their descriptions, when + provided): +

+ +
    +
  • + Choco-solver (CP) ( + + description + + ). +
    • +
  • + + Choco-solver (CP-SAT) + {' '} + ( + + description + + ). +
    • +
  • + + Gecode/Dexter + {' '} + (description). +
    • +
  • + + iZplus + {' '} + (description). +
    • +
  • + JaCoP ( + description). +
    • +
  • + + OptiMathSAT INT + {' '} + (description). +
    • +
  • + + OptiMathSAT INT-SMT2 + {' '} + (description). +
    • +
  • + + OptiMathSAT BV + {' '} + (description). +
    • +
  • + + OR-Tools CP-SAT + {' '} + (description + ). +
    • +
  • + + OR-Tools CP-SAT LS + {' '} + ( + + description + + ). +
    • +
  • + PicatSAT ( + description). +
    • +
  • + + Pumpkin + {' '} + (description). +
    • +
  • + SICStus Prolog ( + description). +
    • +
  • + Yuck ( + description). +
    • +
+

+ In addition, the challenge organisers entered the following + FlatZinc and MiniZinc implementations: +

+ +
    +
  • + Chuffed ( + description). A C++ + FD solver using lazy clause generation. +
    • +
  • + Gecode ( + description). A C++ + FD solver. +
    • +
  • + CpoFzn (description). + Uses{' '} + + IBM ILOG CP Optimizer + {' '} + version 22.11. +
    • +
  • + MZN/Cbc (description + ). Translates to MILP, uses{' '} + Cbc version + 2.10.11. +
    • +
  • + MZN/CPLEX ( + description + ). Translates to MILP, uses{' '} + + IBM ILOG CPLEX Optimizer + {' '} + version 22.11. +
    • +
  • + MZN/Gurobi ( + description + ). Translates to MILP, uses{' '} + Gurobi version + 11.0.3. +
    • +
  • + MZN/HiGHS ( + description + ). Translates to MILP, uses{' '} + HiGHS version 1.7.2. +
    • +
  • + MZN/SCIP ( + description). + Translates to MILP, uses{' '} + SCIP version 9.1.0. +
    • +
+

+ As per the challenge{' '} + rules, these entries are + not eligible for prizes, but do modify the scoring results. +

+ + + +

Global constraint per model

+ +

+ The following table lists the global constraints used by each + model in this year's challenge. In addition, the columns RC + and SBC, respectively, indicate whether the model contains + redundant or/and symmetry breaking constraints. +

+ +
+ +

+ The files on this page are for MiniZinc version{' '} + {info[2024].minizinc}. +

+
+ +
+