Clp

More Samples

CLP’s Samples Directory

The CLP dsitribution includes a number of .cpp sample files. Users are encouraged to use them as starting points for their own CLP projects. The files can be found in the examples directory. For the latest information on compiling and running these samples, please see the file INSTALL in the examples directory. Below is a list of some of the most useful sample files with a short description for each file.

Basic Samples:

Source file	Description
MINIMUMCPP	This is a CLP “Hello, world” program. It reads a problem from an MPS file, and solves the problem.
DEFAULTSCPP	This is one of the simpler driver programs available. It sets tolerances to defaults and is a good place to find straightforward uses of “set” and “get” methods. It also prints out full MPS-like solutions.
DRIVERCPP	This is designed to be a file that a user could modify to get a useful driver program for his or her project. In particular, it demonstrates the use of CLP’s presolve functionality.
NETWORKCPP	This shows the use of non-standard matrices and how to load a problem without the use of MPS files.
TESTBARRIERCPP	This is a basic driver file for the barrier method of CLP, similar to MINIMUMCPP. The barrier method is not currently addressed in this guide.

Advanced Samples:

Source file	Description
DRIVER2CPP	This sample, in addition to some tasks common to other samples, does some advanced message handling and presolve.
DUALCUTSCPP	This sample implements a method of treating a problem as a collection of cuts.
DECOMPOSECPP	This does full Dantzig-Wolfe decomposition. It illustrates the use of many models, adding columns, et cetera.
SPRINTCPP	This solves a long, thin problem by solving smaller subsets. It is a simplified version of work done by one of the authors on aircrew scheduling problems. It shows the use of two models and their synchronization. A more general version can be found in `ClpSolve.cpp`
SPRINT2CPP	This is similar to `sprint.cpp` but is designed for solving large problems with little choice. The idea is that if relatively few variables are fixed, presolve can greatly reduce the problem size so that a series of solves can get close to the optimal solution much faster than would a naïve solve of the full problem.

The remaining Samples listed here are considered unsupported in that they are of a more esoteric nature and are sometimes contributed as a result of an individual’s request.

Source file	Description
TESTBASISCPP	This sample takes a problem, changes any inequality constraints to equality constraints, solves the problem, and creates the optimal basis.
TESTGUBCPP	This sample illustrates the use of the GUB (“Generalized Upper Bound”) technique.
EKKCPP	This sample can be used to compare CLP and OSL. It uses an additional file in the Samples directory, `ekk_interface.cpp`. These sample files are not likely to be interesting to new CLP users who do not have experience with OSL.
HELLOCPP	This sample creates a text-based picture of a matrix on screen (limited to an 80x80 matrix). It’s not terribly useful but it does illustrate one way to step through the elements of a matrix.
PIECECPP	This sample takes a matrix read in by `CoinMpsIo` (can be used to read in MPS files without a solver), deletes every second column and solves the resulting problem.
USEVOLUMECPP	The Volume Algorithm is another solver available as part of the COIN-OR distribution. This sample shows how to use the Volume Algorithm with CLP.

minimum.cpp

This sample is examined in more detail as first example here.

defaults.cpp

This sample begins by reading an MPS file. The default MPS file is COIN/Netlib/Sample/p0033.mps; this can be over-riden by a command-line specification of a (path and) file name). The sample then sets the pivot algorithm to be exact devex. It “gets” the default infeasibility cost and “sets” it to that value (and prints it to standard out). This sort of getting and setting of various parameters constitutes a common theme in this sample, with the purpose of illustrating usage of some of the more common get and set methods available in CLP.

At this point the model is solved by the primal method. A sequence of sets, gets and prints is then followed by a number of calls to methods which give specific information about the status of the problem (for example, the code checks that the current solution has been proven to be optimal by assert(model.isProvenOptimal())).

Next, a copy of the original model is made. More sets and gets are performed to demonstrate the use of additional options (including the setting of the default message handling as well as changing of the “log level” (amount of output)). The model is solved again a number of times between changes of the optimization direction (i.e. changing from min to max or vice versa). The remaining lines of this sample serve to display solution and problem information in much the same way as is done in driver.cpp.

driver.cpp

This sample begins by reading an MPS file. The default MPS file is COIN/Data/Sample/p0033.mps; this can be over-riden by a command-line specification of a (path and) file name). A second command-line argument can specify that either the “primal” or “dual” method (or even the “barrier”, see below) should be used by CLP.

Once the problem has been read, there are two options for how to solve it, one of which must be chosen at compile-time (STYLE1 being defined or not determines this choice). The second manner is more flexible and involves more specific directions being given to CLP, including the ability to specify that the barrier method should be used.

At this point in the sample, the problem is solved by CLP, and some basic ouput is generated. If more output is desired, at compile-time, an exit(0) statement must either be removed or commented. There are two levels of additional output, the first of which is suppressed by a #if 0 directive which may be modified at compile-time if desired. This first level of output only involves non-zero columns, whereas the second provides additional information.

network.cpp

This handy sample reads a network problem generated by netgen, converts it to an LP using CLP’s network matrix type, and solves. This entirely avoids the use of an MPS file, as the LP is built in memory from the network data file created by netgen. Also, the factorization frequency is changed, and the problem is solved more than once (demonstrating the change of optimization sense as well as switching from dual to primal methods).

testBarrier.cpp

This straightfoward sample begins by reading a problem from an MPS file. It then chooses a Cholesky factorization and solves the problem using the predictor corrector barrier method. It then copies the problem and performs a crossover to a simplex solution in the new copy.

dualCuts.cpp

This sample begins with only the equality constraints of a problem. The inequalities are considered to be part of a pool of available cuts in much the same way as is done in integer programming. However, in this case, the cuts are not “generated”, they are simply the inequalities of the problem.

decompose.cpp

driver2.cpp

Common CLP Tasks in the Samples

Below is a listing of a number of common CLP tasks, such as loading a problem from an MPS file, matched with a list of each Sample file which illustrates the performance of a given task.

CLP Task(s)	Method(s)	Sample(s)
Read problem from MPS file	`int readMps(const char *filename)`	DEFAULTSCPP, DRIVERCPP, MINIMUMCPP
Solve by primal method	`int primal()`	DRIVERCPP
Choose pivot rule	`void setPrimalColumnPivotAlgorithm(ClpPrimalColumnPivot &choice)` `void setDualRowPivotAlgorithm(ClpDualRowPivot &choice)`	DEFAULTSCPP
Get/set infeasibility cost	`void setInfeasibilityCost(double value)` `void setInfeasibilityCost(double value)`	DEFAULTSCPP
Get string/”double”/integer information	`bool getStrParam(ClpStrParam key, std::string &value) const` `bool getDblParam(ClpDblParam key, double &value) const` `bool getIntParam (ClpIntParam key, int &value) const`	DEFAULTSCPP
Set maximum number of iterations	`void setMaximumIterations(int value)`	DEFAULTSCPP
Check solution status	`int status() const` `bool isAbandoned() const` `bool isProvenOptimal() const` `bool isProvenPrimalInfeasible() const` `bool isProvenDualInfeasible() const` `bool isPrimalObjectiveLimitReached() const` `bool isDualObjectiveLimitReached() const` `bool isIterationLimitReached() const`

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