# Not-Quite-So-Basic Model Classes

## Pivot Choices

In the dual algorithm, any infeasible basic variable may be chosen to leave the basis. Similarly in the primal algorithm, any non-basic variable with a "bad" reduced cost may be chosen to enter the basis. This choice is probably the most important factor in determining the number of iterations it will take to solve a problem. Clp provides a abstract base class for each case and then instances of each. It is relatively simple for an advanced user to create new instances.

For the dual method the base class is `ClpDualRowPivot`. The two existing instances are `ClpDualRowDantzig` and `ClpDualRowSteepest`. The Dantzig version implements the "standard" pivot rule: choose the most violated basic variable. It is easily dominated by the Steepest instance which should normally be used. The default is to use un-initialized weights where the initial weight for each basic variable is 1.0. If an all-slack basis is being used then these are the correct weights. To use a version which calculates the weights, create an instance and pass it to ClpSimplex model as in the following code fragment:

      ClpDualRowSteepest steep(1); // 0 uninitialized, 1 compute weights
      model.setDualRowPivotAlgorithm(steep);

Similarly for the primal method the base class is `ClpPrimalColumnPivot`. The two existing instances are `ClpPrimalColumnDantzig` and `ClpPrimalColumnSteepest`. The Dantzig version implements "standard" pivot rule: choose the most "violated" non-basic variable. It is dominated by the Steepest instance which should normally be used. The default is to use exact Devex where the initial weight for each non-basic variable is 1.0. Unlike for the dual, this is never the same as normal steepest edge. To use a version which does steepest edge create an instance and pass it to ClpSimplex model as in the following code fragment:

      ClpPrimalColumnSteepest steep(1); // 0 devex, 1 steepest
      model.setPrimalColumnPivotAlgorithm(steep);

The partial pricing scheme (for long, thin problems) currently does not exist. This could be implemented by anyone who is interested.

## Matrix Classes

The next abstract class of interest is `ClpMatrixBase`. CLP encapsulates its knowledge of how a matrix is stored in this class. The default instance of this is the `ClpPackedMatrix` class. This is identical in format to `CoinPackedMatrix`. Below is a diagram summarizing the hierarchy of the most important matrix classes:

The important new methods implemented are for filling a basis, checking validity of elements and faster "times" and "transposeTimes" when the input array is sparse and/or we have a row copy of the matrix. Advanced users should note that not all methods have to be implemented. In particular, `scaling` need not be implemented and `reverseOrderedCopy` can return `NULL` if a row copy does not make sense.

In addition to the default class, there are two others at present: `ClpPlusMinusOneMatrix` and `ClpNetworkMatrix`. As the name implies, the first one is useful when all elements are ±1. In this case multiplies are not needed and more importantly less memory is used and there are fewer cache misses. A class for a matrix where all elements are +1 would be trivial to create. If there were fewer than 64000 rows one could even store row indices as shorts etc.

The use of `ClpPlusMinusOneMatrix` involves some work as one cannot simply read-in an MPS file. The key is to use `loadProblem` to pass in a matrix. So if `matrix` was a `CoinPackedMatrix` one could do the following:

      ClpPlusMinusOneMatrix plusMinus(matrix);
      assert (plusMinus.getIndices()); // would be zero if not +- one
      model.loadProblem(plusMinus,
        lowerColumn,upperColumn,objective,
        lower,upper);

`ClpNetworkMatrix` is similar, but represents a network, thus may only have one element per column. Fortunately, using is is very easy. Given `head` and `tail`, one could do the following:

      ClpNetworkMatrix network(numberColumns,head,tail);
      model.loadProblem(network,
        lowerColumn,upperColumn,objective,
        lower,upper);

Actual code is in `COIN/Clp/Test/unitTest.cpp`. A quick glance at the output of this program shows that use of `ClpNetworkMatrix` gives much faster run times. This is not because of storage issues, but because CLP recognizes the network and uses a network basis factorization which is much faster. However, in this mode CLP is not a genuine network code as it does not take full advantage of the structure by combining operations but it does have the advantage of flexibility.

Other instances are possible. In particular, it should be possible to use the abstract class for column generation or for dynamic matrices which change over time. Minor modifications may be needed but it should work quite smoothly (there is already a dummy "refresh" method which would be used).

## Message Handling

Strictly speaking, message handling is a general COIN topic, but it won't hurt to repeat a few important things here.

A simple user you may wish to turn off some output. This is done with `model.setLogLevel(int value)` where 0 gives nothing and each increase in value switches on more messages. See `ClpMessage.cpp`, `CoinMessage.cpp` and messages to see which messages are at which level. A more sophisticated user may wish to handle messages in a different way. This is done using `passInMessageHandler` with a pointer to a handler of the user's own design. The simplest case would be to use the default handler but use a constructor which writes to file. The code would be:

      FILE * fp; // assumed open
      CoinMessageHandler handler(fp);
      model.passInMessageHandler(&handler);

A still more sophisticated use would be to write a class derived from `CoinMessageHandler` and then override the `print` method. Below follows an example which would print only a message for optimality (or infeasibility):

### Sophisticated message handling

      class DerivedHandler :
       public CoinMessageHandler {
       public:
         virtual int print() ;
       };

       int DerivedHandler::print()
       {
         if (currentSource()=="Clp") {
           if (currentMessage().externalNumber()>=0
           && currentMessage().externalNumber()<4) {
             // finished
             return CoinMessageHandler::print(); // print
           }
         }
         return 0;
       }