PaPILO, a C++14-based software package, provides parallel presolve routines for (mixed integer) linear programming problems. The routines are implemented using templates which allows switching to higher precision or rational arithmetic using the boost multiprecision package.
Additionally to the distribution here under the LGPLv3, PaPILO is also distributed as part of the SCIP Optimization Suite which is available under https://scipopt.org/.
PaPILO can be used as a header-based library and also provides an executable. Using the executable it is possible to presolve and postsolve MILP instances based on files. Additionally, PaPILO can be linked to SCIP, SoPlex, Gurobi, Glop, and HiGHS (https://github.com/ERGO-Code/HiGHS) solvers and act as a frontend. In this setting PaPILO passes the presolved problem to those solvers and applies the postsolve step to the optimal solution. When PaPILO is compiled as part of the SCIP Optimization Suite linking of SoPlex and SCIP solvers is performed automatically. Further, a Julia wrapper is available here.
Note: The original instance of this repository is hosted at git.zib.de and a read-only mirror is available at github.com/scipopt/papilo.
External dependencies that need to be installed by the user are the Intel TBB >= 2020, or TBB from oneAPI runtime library and boost >= 1.65 headers.
The executable additionally requires some of the boost runtime libraries that are not required when PaPILO is used as
a library.
Under the folder external/ there are additional packages that are directly included within PaPILO and have a
liberal open-source license.
If TBB is not found, then the current builds fails. The user has then the option to either enable the automatic download by adding -DTBB_DOWNLOAD to the cmake command or to disable it by adding -DTBB=off instead.
However, it is strongly recommended to install an Intel TBB runtime library since it improves the runtime drastically.
Building PaPILO works with the standard cmake workflow: (we recommend running the make command without specifying the number of jobs that can run simultaneously (no -j n), since this may cause large memory consumption and freeze of the machine)
mkdir build
cd build
cmake ..
make
Building PaPILO with SCIP, HiGHS, SoPlex or OrTools works also with the standard cmake workflow:
mkdir build
cd build
cmake -DSCIP_DIR=PATH_TO_SCIP_BUILD_DIR ..
make
After this, your PaPILO binary should be found in the bin folder.
To install into your system, run sudo make install.
To install PaPILO into a folder, add -DCMAKE_INSTALL_PREFIX=/path/to/install/dir/ to the cmake call and run make install after the build.
If you use a relative path to SCIP, then the reference point is the location of the CMakeLists.txt.
If you want to build PaPILO with a provided Boost version please add one of these option to the cmake command:
-DBOOST_ROOT=../boost_1_72_0
-DBOOST_INCLUDEDIR=../boost_1_72_0/include
Solvers that are found in the system are automatically linked to the executable.
Additionally one can specify the locations of solvers, e.g. with -DSCIP_DIR=<location of scip-config.cmake>, to allow
PaPILO to find them in non-standard locations.
The PaPILO binary provides a list of all available functionality when the help flag -h or --help is specified.
The binary provides the three subcommands solve, presolve, and postsolve. If no solvers are linked the solve subcommand will fail and print an error message.
Next we provide a small example of how the binary can be used to apply presolving and postsolving based on files.
Assuming a problem instance is stored in the file problem.mps the following call will apply presolving with standard settings and write the reduced problem to reduced.mps and all information that is needed for postsolve to the binary archive reduced.postsolve.
papilo presolve -f problem.mps -r reduced.mps -v reduced.postsolve
Not all presolver are able to dual-postsolve the dual solution (and the reduced costs and the basis information). Please use the settings file lp_presolvers_with_basis.set.
Now we can use the reduced problem reduced.mps to obtain a solution
using any solver or from any other source to the file reduced.sol.
The format of the solution should be one value per line given like this:
<variable name> <value>
This is compatible with the solutions given on the MIPLIB 2017 homepage https://miplib.zib.de and with the solutions written by the SCIP solver. Variable names that are not found in the reduced problem are ignored.
The command for applying the postsolve step to the solution reduced.sol is then
papilo postsolve -v reduced.postsolve -u reduced.sol -l problem.sol
Giving the parameter -l problem.sol is optional and will store the solution transformed to the original space under problem.sol.
The output of papilo contains some information about violations and objective value of the solution.
If PaPILO was linked to a suitable solver, then the above can also be achieved by using the solve subcommand like this:
papilo solve -f problem.mps -l problem.sol
This will presolve the problem, pass the reduced problem to a solver, and subsequently transform back the optimal solution returned by the solver and write it to problem.sol.
For the format of the solution and basis files please refer to Format.md
PaPILO provides a templated C++ interface that allows to specify the type used for numerical computations. During configuration time PaPILO scans the system and provides the fastest available numeric types for quadprecision and for exact rational arithmetic in the file
papilo/misc/MultiPrecision.hpp. Including this file will currently introduce the types
papilo::Quad
papilo::Float100
papilo::Float500
papilo::Float1000
papilo::Rational
The numeric type used by PaPILO will be referred to as REAL in the following section. It can be any of the above types as well as simply double for using standard double precision arithmetic.
To avoid confusion with types a short note on types like papilo:Vec and papilo::String.
Those types are aliases for types from the standard library, std::vector and std::string, that possibly use an adjusted allocator. If nothing is altered regarding the allocator then the type papilo::Vec will be exactly the same as std::vector.
It can be changed by adding a partial specialization of papilo::AllocatorTraits<T> after including papilo/misc/Alloc.hpp but before including any other header of PaPILO.
The C++ interface for using PaPILO mainly revolves around the classes
papilo::Presolve<REAL>, which controls the presolving routines,papilo::Problem<REAL>, which holds the problem instance, andpapilo::Postsolve<REAL>, which can transform solutions in the reduced space into solutions for the original problem space. The includes for those classes are underpapilo/core/{Problem,Postsolve,Presolve}.hpp.
The PaPILO binary uses the MPS parsing routine to construct an instance of papilo::Problem<REAL> with the call papilo::MpsParser<REAL>::loadProblem("problem.mps").
For feeding a problem to PaPILO programmatically, there is the class
papilo::ProblemBuilder<REAL>. This class allows to efficiently build an papilo::Problem<REAL> incrementally.
The problem definition that PaPILO supports does not use a row sense, but uses left and right hand sides REAL, that infinite values are always treated the same.
The member functions for initializing the rows and columns are
setNumCols( int ncols )
setNumRows( int nrows )
After calling those functions the problem will have no nonzero entries but the given number of columns and rows. The left and right hand side values of rows the rows are set to
Next the following member functions can be used to alter the bound information about rows and columns as well as the objective coefficients and integrality flags of the columns.
- alter objective coefficient for columns
setObj( int col, REAL val ) - alter bound information for columns
setColLb( int col, REAL lb ) setColLbInf( int col, bool isInfinite ) setColUb( int col, REAL ub ) setColUbInf( int col, bool isInfinite ) - mark column to be restricted to integral values or not
setColIntegral( int col, bool isIntegral ) - alter left and right hand sides of rows
setRowLhsInf( int row, bool isInfinite ) setRowRhsInf( int row, bool isInfinite ) setRowLhs( int row, REAL lhsval ) setRowRhs( int row, REAL rhsval ) - set names of rows, columns, and the problem
setRowName( int row, Str&& name ) setColName( int col, Str&& name ) setProblemName( Str&& name )
Adding nonzeros to the problem can be done with individual nonzero values, row-based, or column-based using the following functions:
/// add the nonzero entries for the given column
addColEntries( int col, int len, const int* rows, const R* vals )
/// add a nonzero entry for the given row and column
addEntry( int row, int col, const REAL& val )
/// add the nonzero entries for the given row
addRowEntries( int row, int len, const int* cols, const R* vals )
All those functions can be called multiple times, but a nonzero entry for a particular column in a particular row should only be added once.
For maximum efficiency the member function
papilo::ProblemBuilder<REAL>::reserve(int nnz, int nrows, int ncols) should be used to reserve all required memory before adding nonzeros.
Finally calling papilo::ProblemBuilder<REAL>::build() will return an instance of papilo::Problem<REAL> with the information that was given to the builder. The builder can be reused afterwards.
For this section we assume a problem instance is stored in a variable problem of type papilo::Problem<REAL>.
In order to presolve a problem instance we need to setup an instance of papilo::Presolve<REAL> and then call papilo::Presolve<REAL>::apply(problem).
The same instance of papilo::Presolve<REAL> can be used for presolving multiple problem instances.
It stores the basic configuration of the presolving routine and constructing it with the default presolvers and settings to presolve the problem is straight forward:
papilo::Presolve<REAL> presolve;
presolve.addDefaultPresolvers();
papilo::PresolveResult<REAL> result = presolve.apply(problem);
After the above call result.status will contain an enum class of the following type:
/// result codes of a presolving routine
enum class PresolveStatus : int
{
/// problem was not changed
kUnchanged = 0,
/// problem was reduced
kReduced = 1,
/// problem was detected to be unbounded or infeasible
kUnbndOrInfeas = 2,
/// problem was detected to be unbounded
kUnbounded = 3,
/// problem was detected to be infeasible
kInfeasible = 4,
};
And result.postsolve contains an instance of the class papilo::Postsolve<REAL>.
First we construct a papilo::Solution<REAL> from a papilo::Vec<REAL> of reduced solution values and an empty instance of papilo::Solution<REAL> to hold the original space solution.
The interface here is not the simplest for the current functionality. It is like this to support
postsolve of dual solutions in the future. The class papilo::Solution<REAL> cannot only hold primal solutions but also dual solution values, even though the postsolve routine does not yet support this step.
Obtaining the original space solution
papilo::Vec<REAL> reducedsolvals;
...
// set up the values of the reduced solution in the reduced index space
...
// create reduced solution and original solution
papilo::Solution<REAL> reducedsol(std::move(reducedsolvals));
papilo::Solution<REAL> origsol;
// transform the reduced solution into the original problem space
PostsolveStatus status = result.postsolve.undo(reducedsol, origsol, postsolve_storage);
The value of status is PostsolveStatus::kOk if everything worked or PostsolveStatus::kFail otherwise.
If everything worked then the vector origsol.primal contains the primal solution values in the original problem space.
There are several parameters that can be adjusted to influence the behavior during presolving.
All the parameters and their default values are listed in the file parameters.txt.
Adjusting a parameter via the command line when using the PaPILO exectuable works like this:
papilo solve -f problem.mps -l problem.sol --presolve.randomseed=42
This call will use an adjusted random seed for the presolve routine.
Alternatively a file with the same format as parameters.txt can be used to set multiple parameters by
passing the setting file with the -p/--parameter-settings flag.
Passing the --print-params flag will print the parameters in a format similar to the one of parameters.txt before starting presolving.
The printed parameters will have the values they were set to, not the default values.
For adjusting the parameters programatically there are two ways.
The first way is to obtain an instance of papilo::ParameterSet by calling papilo::Presolve<REAL>::getParameters().
It is important to call this member function after all presolvers have been added to the papilo::Presolve<REAL> class.
Otherwise, not all parameters are available, e.g. the ones that are added by individual presolvers.
Now we can call papilo::ParameterSet::setParameter( key, val ) to set parameters to their desired values.
If we want to adjust the random seed programmatically this would look like
papilo::Presolve<REAL> presolve;
...
// add all presolvers
...
papilo::ParameterSet paramset = presolve.getParameters();
paramset.setParameter("presolve.randomseed", 42);
The function papilo::ParameterSet::setParameter() will throw exceptions if anything goes wrong,
e.g. if the parameter key was not recognized or the type of the value is not suitable.
Possible exceptions are of the types std::out_of_range, std::domain_error, or std::invalid_argument and contain a suitable error message.
For debugging it can be helpful to print the parameters stored within a papilo::ParameterSet which can be achieved by calling paramset.printParams(std::cout) and produces an output similar to the one in parameters.txt.
The second way to set a subset of parameters is by directly accessing the instance of papilo::PresolveOptions that is stored within each instance of papilo::Presolve<REAL>.
Setting the random seed with this method can simply be achieved by presolve.getPresolveOptions().randomseed = 42.
The caveat with directly accessing the papilo::PresolveOptions is, that parameters added by individual presolvers cannot be set and that no error checking is performed in case the user sets a parameter to an invalid value.
Nevertheless, this can be convenient for setting basic things like tolerances, time limits, and thread limits.
Adding a presolver to PaPILO requires the following steps. First create a class for your presolver that inherits publically from papilo::PresolveMethod<REAL>.
In the constructor at least adjust the name and the timing of the new presolver.
For the constraint propagation presolver this is done in the constructor by the following calls:
this->setName( "propagation" );
this->setTiming( PresolverTiming::kFast );
Use the following rules to set a suitable value for PresolverTiming.
-
PresolverTiming::kFastfor presolvers that work only on altered parts of the problem. E.g. the constraint propagation presolver will only look at rows for which the activity was adjusted -
PresolverTiming::kMediumfor presolvers that run in$O(n \log n)$ of the problem size (number of rows/columns/nonzeros). E.g. the parallel row/column presolvers compute hash values and sort on the order of the rows and columns. Technically the runtime becomes quadratic in the worst case when many collisions occur, but practically the presolvers are fast. They are the slowest in the category of medium timing presolvers included in the default. -
PresolverTiming::kExhaustivefor presolvers that neither fit into the fast or medium categories
In addition, depending on the presolve method, it might be good to add restrictions on what type of problems a presolver is called.
For this the setType(PresolveType) member function of papilo::PresolveMethod<REAL> should be used.
If the type is set to PresolverType::KIntegralCols or PresolverType::kContinuousCols then the presolver only runs if integral or continuous columns are present respectively. The type PresolverType::kMixedCols runs only if both integral and continuous columns are present which is suitable for the implied integer detection presolver.
For presolve methods that require internal state two additional virtual member functions need to be overriden.
An example for this can be seen in the papilo::Probing<REAL> presolver.
The probing presolver stores how often a variable has been probed between calls.
For this it overrides the papilo::PresolveMethod<REAL>::initialize() member function which clears the internal data and
adjusts it to the given problems dimension. The function must return a boolean value.
The value false indicates that the presolver does not need to be informed in case the index space of the columns changes
and the value true indicates the opposite. When true is returned in the initialize member function papilo::PresolveMethod<REAL>::compress()
should additionally be overriden. The function is called with a mapping for the rows and columns and is called when
PaPILO compresses the index space of the problem.
In the compress callback the probing presolver uses the given column mapping to compress the vector that stores how often each variable has been probed.
If the presolve method needs to add parameters that can be adjusted via the papilo::ParameterSet, then the member function papilo::PresolveMethod<REAL>::addPresolverParams() should be overridden.
The member function papilo::PresolveMethod<REAL>::execute() is a pure virtual function and therefore must be implemented for every presolve method.
This is where the presolver actually runs. The method grants read-only access to the papilo::Problem<REAL> that is being presolved
and grants write access to an instance of papilo::Reductions<REAL>. A presolve method should scan the problem for possible reductions and add them
to the reductions class. This is only the broad picture and there are some more details that we omit here for brevity.
Looking at some of the default implemented presolvers can help for understanding further details and also the resources in the next section.
Finally when a new presolve method is implemented it needs to be added to the papilo::Presolve<REAL> instance that is used for presolving.
Assuming the new presolve method is called MyPresolveMethod this is achiev
ed by the following call:
presolve.addPresolver( std::unique_ptr<papilo::PresolveMethod<REAL>>( new MyPresolveMethod<REAL>() ) );
Getting the PaPILO binary to call your presolver could be achieved by adding an include for your presolver in papilo/core/Presolve.hpp and then adding it together with the other default presolvers in the member function papilo::Presolve<REAL>::addDefaultPresolvers().
You can run your performance tests by executing the command make test in the root directory or sent the jobs to a slurm cluster with make testcluster.
The options of the make command are explained here:
make test EXECUTABLE=build/bin/papilo TEST=short SETTINGS=default
EXECUTABLELocation of the papilo executable from the root repositoryTESTall instances of the current experiments. File should be located at check/testset. For an example please refer to short.test.SETTINGSsettings file located in the settings folderPAPILO_SOLVE_COMMANDsolveorpresolveTIMEtimelimitMEMmaximal memoryOUTPUTDIRoutput directory where the results are storedQUEUEqueue (only make testcluster)
The make command generates an .out and .err file for each instance specified in the TEST file and in case of make test also an overall out file is created containing logs of all instances.
If make testcluster is used the overall out file must be generated with ./evalcheck_cluster.sh results/name.eval
Note that the options all have default values. Executing a plain make test will run experiments on the short testset.
Any publication for which PaPILO is used must include an acknowledgement and a reference to the following article:
@article{GGHpapilo,
author = {Ambros Gleixner and Leona Gottwald and Alexander Hoen},
publisher = {INFORMS Journal on Computing},
title = {{PaPILO}: A Parallel Presolving Library for Integer and Linear Programming with Multiprecision Support},
year = {2023},
journal = {INFORMS Journal on Computing},
doi = {10.1287/ijoc.2022.0171.cd},
url = {https://github.com/INFORMSJoC/2022.0171},
}
Most of the presolve methods implemented in PaPILO are described in the paper "Presolve Reductions in Mixed Integer Programming" by Achterberg et al. which is available under https://opus4.kobv.de/opus4-zib/files/6037/Presolve.pdf.
Some details on how PaPILO works internally are presented in a talk given during the SCIP workshop 2020 which has been recorded and is available under https://www.youtube.com/watch?v=JKAyyWXGeQM.
To avoid confusion about licensing a short note on the LGPLv3. This note is just an explanation and legally only the license text itself is of relevance.
When PaPILO is used as a header-based library then only section 3 of LGPLv3 is relevant, and not section 4. Therefore PaPILO in that setting could be used in a software that is distributed under the terms of a different license when the conditions of section 3 are met, which are
a) Give prominent notice with each copy of the object code (refers to binary distributions of your software) that the Library (refers to PaPILO) is used in it and that the Library and its use are covered by this License (refers to the LGPLv3).
b) Accompany the object code with a copy of the GNU GPL and this license document.
Modifications of PaPILO itself, however, must be distributed under LGPLv3.
Ambros Gleixner (@ambros-gleixner) — project head
Alexander Hoen (@alexhoen) — main developer
Ivet Galabova (@galabovaa) — initial draft for dual postsolve
Leona Gottwald (@lgottwald) — creator and former main developer
Katrin Halbig (@khalbig) — presolver for GCD based reductions on inequalities (SimplifyIneq), strengthening in DualFix
Gabriel Kressin (@GabrielKP) — numerical statistics, testing
Julian Manns (@ju-manns) — build system
Anass Meskini — general development and contributions to substitution presolver in terms of internship
Daniel Rehfeldt (@dRehfeldt) — core data structures and MPS parsing
Franziska Schlösser (@fschloesser) — build system