Copyright © 2005 IBM Coportation
Table of Contents
List of Tables
List of Examples
Table of Contents
The COIN ^{[1]} Branch and Cut solver (CBC) is an opensource mixedinteger program (MIP) solver written in C++. CBC is intended to be used primarily as a callable library to create customized branchandcut solvers. A basic, standalone executable version is also available. CBC is an active opensource project led by John Forrest at www.coinor.org.
The primary users of CBC are expected to be developers implementing customized branchandcut algorithms in C++ using CBC as a library. Consequently, this document assumes a working knowledge of C++, including basic objectoriented programming terminology, and familiarity with the fundamental concepts of linear programming (LP) and mixed integer programming (MIP).
CBC relies on other parts of the COIN repository. CBC needs a LP solver and relies on the COIN Open Solver Inteface (OSI) to communicate with the user's choice of solver. Any LP solver with an OSI interface can be used with CBC. The LP solver expected to be used most commonly is COIN's native linear program solver, CLP. For cut generators, CBC relies on the COIN Cut Generation Library (CGL). Any cut generator written to CGL standards can be used with CBC. Some of the cut generators in CGL rely on other parts of COIN, e.g., CGL's Gomory cut generator rely on the factorization functionality of CoinFactorization. This document assumes basic familiarity with OSI and CGL.
Technically speaking, CBC accesses the solver (and sometime the model and data it contains) through an OSISolverInterface. For the sake of simplicity, we will refer to the OsiSolverInterface as "the solver" in this document, rather than "the standard application programming interface to the solver." We hope any confusion caused by blurring this distinction will be mitigated by the shorter sentences.
In summary, readers should have the following prerequisites:
Before examining CBC in more detail, we tersely describe the basic branchandcut algorithm by way of example, (which should really be called branchandcutandbound) and show the major C++ class(es) in CBC related to each step. The major CBC classes, labeled (A) through (F), are described in Table 1.1.
Step 1. (Bound) Given a MIP model to minimize where some variables must take on integer values (e.g., 0, 1, or 2), relax the integrality requirements (e.g., consider each "integer" variable to be continuous with a lower bound of 0.0 and an upper bound of 2.0). Solve the resulting linear model with an LP solver to obtain a lower bound on the MIP's objective function value. If the optimal LP solution has integer values for the MIP's integer variables, we are finished. Any MIPfeasible solution provides an upper bound on the objective value. The upper bound equals the lower bound; the solution is optimal.
Step 2. (Branch) Otherwise, there exists an "integer" variable with a nonintegral value. Choose one nonintegral variable (e.g., with value 1.3) (A)(B) and branch. Create two ^{[2]} nodes, one with the branching variable having an upper bound of 1.0, and the other with the branching variable having a lower bound of 2.0. Add the two nodes to the search tree.
While (search tree is not empty) {
Step 3. (Choose Node) Pick a node off the tree (C)(D)
Step 4. (Reoptimize LP) Create an LP relaxation and solve.
Step 5. (Bound) Interrogate the optimal LP solution, and try to prune the node by one of the following.
Step 6. (Branch) If we were unable to prune the node, then branch. Choose one nonintegral variable to branch on (A)(B). Create two nodes and add them to the search tree. }
This is the outline of a "branchandbound" algorithm. If in optimizing the linear programs, we use cuts to tighten the LP relaxations (E)(F), then we have a "branchandcut" algorithm. (Note, if cuts are only used in Step 1, the method is called a "cutandbranch" algorithm.)
Table 1.1. Associated Classes
Note  Class name  Description 

(A)  CbcBranch...  These classes define the nature of MIP's discontinuity. The simplest discontinuity is a variable which must take an integral value. Other types of discontinuities exist, e.g., lotsizing variables. 
(B)  CbcNode  This class decides which variable/entity to branch on next. Even advanced users will probably only interact with this class by setting CbcModel parameters ( e.g., priorities). 
(C)  CbcTree  All unsolved models can be thought of as being nodes on a tree where each node (model) can branch two or more times. The interface with this class is helpful to know, but the user can pretty safely ignore the inner workings of this class. 
(D)  CbcCompare...  These classes are used in determine which of the unexplored nodes in the tree to consider next. These classes are very small simple classes that can be tailored to suit the problem. 
(E)  CglCutGenerators  Any cut generator from CGL can be used in CBC. The cut generators are passed to CBC with parameters which modify when each generator will be tried. All cut generators should be tried to determine which are effective. Few users will write their own cut generators. 
(F)  CbcHeuristics  Heuristics are very important for obtaining valid solutions quickly. Some heuristics are available, but this is an area where it is useful and interesting to write specialized ones. 
There are a number of resources available to help new CBC users get started. This document is designed to be used in conjunction with the files in the Samples subdirectory of the main CBC directory (COIN/Cbc/Samples). The Samples illustrate how to use CBC and may also serve as useful starting points for user projects. In the event that either this document or the available Doxygen content conflicts with the observed behavior of the source code, the comments in the header files, found in COIN/Cbc/include, are the ultimate reference.
^{[1] } The complete acronym is "COINOR" which stands for the Compuational Infrastructure for Operations Research. For simplicity (and in keeping with the directory and function names) we will simply use "COIN".
^{[2] } The current implementation of CBC allow two branches to be created. More general number of branches could be implemented.
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The main class in CBC is CbcModel. The CbcModel class is where most of the parameter setting is done. The absolute minimum number of actions taken with CbcModel is two,
The first sample program shows how to perform simple branchandbound with CBC. This program is short enough to present in full. Most of the remaining examples will take the form of small code fragments. The complete code for all the examples in this Guide can be found in the CBC Samples directory, COIN/Cbc/Samples.
Example 2.1. minimum.cpp
// Copyright (C) 2005, International Business Machines // Corporation and others. All Rights Reserved. #include "CbcModel.hpp" // Using CLP as the solver #include "OsiClpSolverInterface.hpp" int main (int argc, const char *argv[]) { OsiClpSolverInterface solver1; // Read in example model in MPS file format // and assert that it is a clean model int numMpsReadErrors = solver1.readMps("../../Mps/Sample/p0033.mps",""); assert(numMpsReadErrors==0); // Pass the solver with the problem to be solved to CbcModel CbcModel model(solver1); // Do complete search model.branchAndBound(); /* Print the solution. CbcModel clones the solver so we need to get current copy from the CbcModel */ int numberColumns = model.solver()>getNumCols(); const double * solution = model.bestSolution(); for (int iColumn=0;iColumn<numberColumns;iColumn++) { double value=solution[iColumn]; if (fabs(value)>1.0e7&&model.solver()>isInteger(iColumn)) printf("%d has value %g\n",iColumn,value); } return 0; }
The program in Example 2.1 creates a OsiClpSolverInterface solver interface (i.e., solver1), and reads an MPS file. If there are no errors, the program passes the problem to CbcModel which solves the problem using the branchandbound algorithm. The part of the program which solves the problem is very small (one line!) but before that one line, the LP solver (i.e., solver1) had to be created and populated with the problem. After that one line, the results were printed out.
The program in Example 2.1 illustrates the dependency of CBC on the OsiSolverInterface class. The constructor of CbcModel takes a pointer to an OsiSolverInterface (i.e., a solver). The CbcModel clones the solver, and uses its own instance of the solver. The CbcModel's solver and the original solver (e.g., solver1) are not in sync unless the user synchronizes them. The user can always access the CbcModel's solver through the model() class. To synchronize the two solvers, explicitly refreshing the original, e.g.,
solver1 = model.solver();
CbcModel's method solver() returns a pointer to CBC's cloned solver.
For convenience, many of the OSI methods to access problem data have identical method names in CbcModel. (It's just more convenient to type model.getNumCols() rather than model.solver()>getNumCols()). The CbcModel refreshes its solver at certain logical points during the algorithm. At these points, the information from the CbcModel model will match the information from the model.solver(). Elsewhere, the information may vary. For instance, the method CbcModel::bestSolution() will contain the best solution so far, the OSI method getColSolution() may not. In this case, it is safer to use CbcModel::bestSolution().
While all the OSI methods used in minimum.cpp have equivalent methods in CbcModel, there are some OSI methods which do not. For example, if the program produced a lot of undesired output, one might add the line
model.solver()>setHintParam(OsiDoReducePrint,true,OsiHintTry);
to reduce the output. There is no setHintParam() method in CbcModel.
Optimality can be checked through a call to model.isProvenOptimal(). Also available are isProvenInfeasible(), isSolutionLimitReached(), isNodeLimitReached() or the feared isAbandoned(). There is also int status() which returns 0 if finished (which includes the case when the algorithm is finished because it has been proved infeasible), 1 if stopped by user, and 2 if difficulties arose.
In addition to these CbcModel methods, solution values can be accessed via OSI methods. The OSI methods pick up the current solution in the CBCModel. The current solution will match the best solution found so far if called after branchAndBound() and a solution was found.
Table 2.1. Methods for Getting Solution Information from OSI
Purpose  Name  Notes 

Primal column solution  const double * getColSolution()  The OSI method will return the best solution found thus far, unless none has been found. It is safer to use CbcModel version, CbcModel::bestSolution() 
Dual row solution  const double * getRowPrice()  Identical CbcModel version available, CbcModel::getRowPrice(). 
Primal row solution  const double * getRowActivity()  Identical CbcModel version available, CbcModel::getRowActivity(). 
Dual column solution  const double * getReducedCost()  Identical CbcModel version available, CbcModel::gtReducedCost(). 
Number of rows in model  int getNumRows()  Identical CbcModel version available, CbcModel::getNumRows(). Note: the number of rows can change due to cuts. 
Number of columns in model  int getNumCols()  Identical CbcModel version available, CbcModel::getNumCols(). 
Most of the parameter setting in CBC is done through CbcModel methods. The most commonly used set and get methods are listed in Table 2.2.
Table 2.2. Useful Set and Get Methods in CbcModel
Method(s)  Description 

bool setMaximumNodes(int value) int getMaximumNodes() const bool setMaximumSeconds(double value) double getMaximumSeconds() bool setMaximumSolutions(double value) double getMaximumSolutions() const  These set methods tell CBC to stop after a given number of nodes, seconds, or solutions is reached. The get methods return the corresponding values. 
bool setIntegerTolerance(double value) const double getIntegerTolerance() const  An integer variable is deemed to be at an integral value if it is no further than this value (tolerance) away. 
bool setAllowableGap(double value) double getAllowableGap() const bool setAllowablePercentageGap(double value) double getAllowablePercentageGap() const bool setAllowableFractionGap(double value) double getAllowableFractionGap() const  CbcModel returns if the gap between the best known solution and the best possible solution is less than this value, or as a percentage, or a fraction. 
void setNumberStrong(double value) int numberStrong() ^{[a]} const  These methods set or get the maximum number of candidates at a node to be evaluated for strong branching. 
void setPrintFrequency(int value) int printFrequency() const  Controls the number of nodes evaluated between status prints. Print frequency has a very slight overhead, if value is small. 
int getNodeCount() const  Returns number of nodes evaluated in the search. 
int numberRowsAtContinuous() const  Returns number of rows in the problem when handed to the solver (i.e., before cuts where added). Commonly used in implementing heuristics. 
int numberIntegers() const const int * integerVariable() const  Returns number of integer variables and an array specifying them. 
bool isBinary(int colIndex) const bool isContinuous(int colIndex) const bool isInteger(int colIndex) const  Returns information on variable colIndex. OSI methods can be used to set these attributes (before handing the model to CbcModel). 
double getObjValue() const  This method returns the best objective value so far. 
double getCurrentObjValue() const  This method returns the current objective value. 
const double * getObjCoefficients() const  This method return the objective coefficients. 
const double * getRowLower() const const double * getRowUpper() const const double * getColLower() const const double * getColUpper() const  These methods return the lower and upper bounds on row and column activities. 
const CoinPackedMatrix * getMatrixByRow() const  This method returns a pointer to a row copy of matrix stored as a CoinPackedMatrix which can be further examined. 
const CoinPackedMatrix * getMatrixByCol() const  This method returns a pointer to a column copy of matrix stored as a CoinPackedMatrix which can be further examined. 
CoinBigIndex getNumElements() const^{[b]}  Returns the number of nonzero elements in the problem matrix. 
void setObjSense(double value) double getObjSense() const  These methods set and get the objective sense. The parameter value should be +1 to minimize and 1 to maximize. 
^{[a] } This methods (and some of the other) do not follow the "get" convention. The convention has changed over time and there are still some inconsistencies to be cleaned up. ^{[b] } CoinBigIndex is a typedef which in most cases is the same as int. 
CbcModel is extremely flexible and customizable. The class structure of CBC is designed to make the most commonly desired customizations of branchandcut possible. These include:
To enable this flexibility, CbcModel uses other classes in CBC (some of which are virtual and may have multiple instances). Not all classes are created equal. The two tables below list in alphabetical order the classes used by CbcModel that are of most interest and of least interest.
Table 2.3. Classes Used by CbcModel  Most Useful
Class name  Description  Notes 

CbcCompareBase  Controls which node on the tree is selected.  The default is CbcCompareDefault. Other comparison classes in CbcCompareActual.hpp include CbcCompareDepth and CbcCompareObjective. Experimenting with these classes and creating new compare classes is easy. 
CbcCutGenerator  A wrapper for CglCutGenerator with additional data to control when the cut generator is invoked during the tree search.  Other than knowing how to add a cut generator to CbcModel, there is not much the average user needs to know about this class. However, sophisticated users can implement their own cut generators. 
CbcHeuristic  Heuristic that attempts to generate valid MIPsolutions leading to good upper bounds.  Specialized heuristics can dramatically improve branchandcut performance. As many different heuristics as desired can be used in CBC. Advanced users should consider implementing custom heuristics when tackling difficult problems. 
CbcObject  Defines what it means for a variable to be satisfied. Used in branching.  Virtual class. CBC's concept of branching is based on the idea of an "object". An object has (i) a feasible region, (ii) can be evaluated for infeasibility, (iii) can be branched on, e.g., a method of generating a branching object, which defines an up branch and a down branch, and (iv) allows comparison of the effect of branching. Instances of objects include CbcSimpleInteger, CbcSimpleIntegerPseudoCosts, CbcClique, CbcSOS (type 1 and 2), CbcFollowOn, and CbcLotsize. 
OsiSolverInterface  Defines the LP solver being used and the LP model. Normally a pointer to the desired OsiSolverInteface is passed to CbcModel before branch and cut.  Virtual class. The user instantiates the solver interface of their choice, e.g., OsiClpSolverInterface. 
There is not much about the classes listed in Table 2.4 that the average user needs to know about.
Table 2.4. Classes Used by CbcModel  Least Useful
Class name  Description  Notes 

CbcBranchDecision  Used in choosing which variable to branch on, however, most of the work is done by the definitions in CbcObject.  Defaults to CbcBranchDefaultDecision. 
CbcCountRowCut  Interface to OsiRowCut. It counts the usage so cuts can gracefully vanish.  See OsiRowCut for more details. 
CbcNode  Controls which variable/entity is selected to be branch on.  Controlled via CbcModel parameters. Information from CbcNode can be useful in creating customized node selection rules. 
CbcNodeInfo  Contains data on bounds, basis, etc. for one node of the search tree.  Header is located in CbcNode.hpp. 
CbcTree  Defines how the search tree is stored.  This class can be changed but it is not likely to be modified. 
CoinMessageHandler  Deals with message handling  The user can inherit from CoinMessageHandler to specialize message handling. 
CoinWarmStartBasis  Basis representation to be used by solver 
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The order in which the nodes of the search tree are explored can strongly influence the performance of branchandcut algorithms. CBC give users complete control over the search order, including the ability to dynamically change the node selection logic as the search progresses. The search order is controlled via the CbcCompare... class, and its method test(). Dynamic changes can be made whenever
CBC provides an abstract base class, CbcCompareBase, and implementations of several commonly used node selection strategies as Compare Classes, see Table 3.1.
Table 3.1. Compare Classes Provided
Class name  Description 

CbcCompareDepth  This will always choose the node deepest in tree. It gives minimum tree size but may take a long time to find the best solution. 
CbcCompareObjective  This will always choose the node with the best objective value. This may give a very large tree. It is likely that the first solution found will be the best and the search should finish soon after the first solution is found. 
CbcCompareDefault  This is designed to do a mostly depthfirst search until a solution has been found. It then use estimates that are designed to give a slightly better solution. If a reasonable number of nodes have been explored (or a reasonable number of solutions found), then this class will adopt a breadthfirst search (i.e., making a comparison based strictly on objective function values) unless the tree is very large, in which case it will revert to depthfirst search. A better description of CbcCompareUser is given below. 
CbcCompareEstimate  When pseudo costs are invoked, CBC uses the psuedo costs to guess a solution. This class uses the guessed solution. 
It is relatively simple for a user to create a customized node selection by creating a new compare class instances. The code in Example 3.1 describes how to build a new comparison class and the reasoning behind it. The complete source can be found in CbcCompareUser.hpp and CbcCompareUser.cpp, located in the CBC Samples directory. Besides the constructor, the only method the user must implement in CbcCompare is bool test(CbcNode* x, CbcNode* y)) which returns true if node y is preferred over node x. In the test() method, information from CbcNode can easily be used. Table 3.2 lists some commonly used methods to access information at a node.
Table 3.2. Information Available from CbcNode
double objectiveValue() const  Value of objective at the node. 
int numberUnsatisfied() const  Number of unsatisfied integers (assuming branching object is an integer  otherwise it might be number of unsatisfied sets). 
int depth() const  Depth of the node in the search tree. 
double guessedObjectiveValue() const  Returns the guessed objective value, if the user was setting this (e.g., if using pseudo costs). 
int way() const  The way which branching would next occur from this node (for more advanced use). 
int variable() const  The branching "variable" (associated with the CbcBranchingObject  for more advanced use). 
The node desired in the tree is often a function of the how the search is progressing. In the design of CBC, there is no information on the state of the tree. The CBC is designed so that the method newSolution() is called whenever a solution is found and the method every1000Nodes() is called every 1000 nodes. When these methods are called, the user has the opportunity to modify the behavior of test() by adjusting their common variables (e.g., weight_). Because CbcNode has a pointer to the model, the user can also influence the search through actions such as changing the maximum time CBC is allowed, once a solution has been found (e.g., CbcModel::setMaximumSeconds(double value)). In CbcCompareUser.cpp of the COIN/Cbc/Samples directory, four items of data are used.
1) The number of solutions found so far
2) The size of the tree (defined to be the number of active nodes)
3) A weight, weight_, which is initialized to 1.0
4) A saved value of weight, saveWeight_ (for when weight is set back to 1.0 for special reason)
Initially, weight_ is 1.0 and the search is biased towards depth first. In fact, test() prefers y if y has fewer unsatisfied variables. In the case of a tie, test() prefers the node with the greater depth in tree. The full code for the CbcCompareUser::test() method is given in Example 3.1.
Example 3.1. CbcCompareUser::test()
// Returns true if y better than x bool CbcCompareUser::test (CbcNode * x, CbcNode * y) { if (weight_==1.0) { // before solution if (x>numberUnsatisfied() > y>numberUnsatisfied()) return true; else if (x>numberUnsatisfied() < y>numberUnsatisfied()) return false; else return x>depth() < y>depth(); } else { // after solution. // note: if weight_=0, comparison is based // solely on objective value double weight = CoinMax(weight_,0.0); return x>objectiveValue()+ weight*x>numberUnsatisfied() > y>objectiveValue() + weight*y>numberUnsatisfied(); } }
CBC calls the method newSolution() after a new solution is found. The method newSolution() interacts with test() by means of the variable weight_. If the solution was achieved by branching, a calculation is made to determine the cost per unsatisfied integer variable to go from the continuous solution to an integer solution. The variable weight_ is then set to aim at a slightly better solution. From then on, test() returns true if it seems that y will lead to a better solution than x. This source for newSolution() in given in Example 3.2.
Example 3.2. CbcCompareUser::newSolution()
// This allows the test() method to change behavior by resetting weight_. // It is called after each new solution is found. void CbcCompareUser::newSolution(CbcModel * model, double objectiveAtContinuous, int numberInfeasibilitiesAtContinuous) { if (model>getSolutionCount()==model>getNumberHeuristicSolutions()) return; // The number of solutions found by any means equals the // number of solutions, so this solution was found by rounding. // Ignore it. // set weight_ to get close to this solution double costPerInteger = (model>getObjValue()objectiveAtContinuous)/ ((double) numberInfeasibilitiesAtContinuous); weight_ = 0.98*costPerInteger; // this aims for a solution // slightly better than known. // why 0.98? why not?! Experiment yourself. saveWeight_=weight_; // We're going to switching between depthfirst and breadthfirst // branching strategies, depending on what we find in the tree. // When doing depth first, we'll want to retrieve this weight. // So, let's save it. numberSolutions_++; if (numberSolutions_>5) weight_ =0.0; // comparison in test() will be // based strictly on objective value. }
As the search progresses, the comparison can be modified. If many nodes (or many solutions) have been generated, then weight_ is set to 0.0 leading to a breadthfirst search. Breadthfirst search can lead to an enormous tree. If the tree size is exceeds 10000, it may be desirable to return to a search biased towards depth first. Changing the behavior in this manner is done by the method every1000Nodes shown in Example 3.3.
Example 3.3. CbcCompareUser::every1000Nodes()
// This allows the test() method to change behavior every 1000 nodes. bool CbcCompareUser::every1000Nodes(CbcModel * model, int numberNodes) { if (numberNodes>10000) weight_ =0.0; // compare nodes based on objective value // get size of tree treeSize_ = model>tree()>size(); if (treeSize_>10000) { // set weight to reduce size most of time if (treeSize_>20000) weight_=1.0; else if ((numberNodes%4000)!=0) // Flipflop between the strategies. // Why 4000? Why not? Experiment yourself. weight_=1.0; else weight_=saveWeight_; } return numberNodes==11000; // resort if first time }
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In practice, it is very useful to get a good solution reasonably fast. Any MIPfeasible solution produces an upper bound, and a good bound will greatly reduce the run time. Good solutions can satisfy the user on very large problems where a complete search is impossible. Obviously, heuristics are problem dependent, although some do have more general use. At present there is only one heuristic in CBC itself, CbcRounding. Hopefully, the number will grow. Other heuristics are in the COIN/Cbc/Samples directory. A heuristic tries to obtain a solution to the original problem so it only needs to consider the original rows and does not have to use the current bounds. CBC provides an abstract base class CbcHeuristic and a rounding heuristic in CBC.
This chapter describes how to build a greedy heuristic for a set covering problem, e.g., the miplib problem fast0507. A more general (and efficient) version of the heuristic is in CbcHeuristicGreedy.hpp and CbcHeuristicGreedy.cpp located in the COIN/Cbc/Samples directory, see Chapter 8, More Samples .
The greedy heuristic will leave all variables taking value one at this node of the tree at value one, and will initially set all other variables to value zero. All variables are then sorted in order of their cost divided by the number of entries in rows which are not yet covered. (We may randomize that value a bit so that ties will be broken in different ways on different runs of the heuristic.) The best one is choosen, and set to one. The process is repeated. Because this is a set covering problem (i.e., all constraints are ≥), the heuristic is guaranteed to find a solution (but not necessarily an improved solution). The speed of the heuristic could be improved by just redoing those affected, but for illustrative purposes we will keep it simple. (The speed could also be improved if all elements are 1.0).
The key CbcHeuristic method is int solution(double & solutionValue, double * betterSolution). The solution() method returns 0 if no solution found, and returns 1 if a solution is found, in which case it fills in the objective value and primal solution. The code in CbcHeuristicGreedy.cpp is a little more complicated than this following example. For instance, the code here assumes all variables are integer. The important bit of data is a copy of the matrix (stored by column) before any cuts have been made. The data used are bounds, objective and the matrix plus two work arrays.
Example 4.1. Data
OsiSolverInterface * solver = model_>solver(); // Get solver from CbcModel const double * columnLower = solver>getColLower(); // Column Bounds const double * columnUpper = solver>getColUpper(); const double * rowLower = solver>getRowLower(); // We know we only need lower bounds const double * solution = solver>getColSolution(); const double * objective = solver>getObjCoefficients(); // In code we also use min/max double integerTolerance = model_>getDblParam(CbcModel::CbcIntegerTolerance); double primalTolerance; solver>getDblParam(OsiPrimalTolerance,primalTolerance); int numberRows = originalNumberRows_; // This is number of rows when matrix was passed in // Column copy of matrix (before cuts) const double * element = matrix_.getElements(); const int * row = matrix_.getIndices(); const CoinBigIndex * columnStart = matrix_.getVectorStarts(); const int * columnLength = matrix_.getVectorLengths(); // Get solution array for heuristic solution int numberColumns = solver>getNumCols(); double * newSolution = new double [numberColumns]; // And to sum row activities double * rowActivity = new double[numberRows];
The newSolution is then initialized to the rounded down solution.
Example 4.2. Initialize newSolution
for (iColumn=0;iColumn<numberColumns;iColumn++) { CoinBigIndex j; double value = solution[iColumn]; // Round down integer if (fabs(floor(value+0.5)value)<integerTolerance) value=floor(CoinMax(value+1.0e3,columnLower[iColumn])); // make sure clean value = CoinMin(value,columnUpper[iColumn]); value = CoinMax(value,columnLower[iColumn]); newSolution[iColumn]=value; if (value) { double cost = objective[iColumn]; newSolutionValue += value*cost; for (j=columnStart[iColumn]; j<columnStart[iColumn]+columnLength[iColumn];j++) { int iRow=row[j]; rowActivity[iRow] += value*element[j]; } } }
At this point some row activities are below their lower bound. To correct the infeasibility, the variable which is cheapest in reducing the sum of infeasibilities is found and updated, and the process repeats. This is a finite process. (The implementation could be faster, but is kept simple for illustrative purposes.)
Example 4.3. Create Feasible newSolution from Initial newSolution
while (true) { // Get column with best ratio int bestColumn=1; double bestRatio=COIN_DBL_MAX; for (int iColumn=0;iColumn<numberColumns;iColumn++) { CoinBigIndex j; double value = newSolution[iColumn]; double cost = direction * objective[iColumn]; // we could use original upper rather than current if (value+0.99<columnUpper[iColumn]) { double sum=0.0; // Compute how much we will reduce infeasibility by for (j=columnStart[iColumn]; j<columnStart[iColumn]+columnLength[iColumn];j++) { int iRow=row[j]; double gap = rowLower[iRow]rowActivity[iRow]; if (gap>1.0e7) { sum += CoinMin(element[j],gap); if (element[j]+rowActivity[iRow]<rowLower[iRow]+1.0e7) { sum += element[j]; } } if (sum>0.0) { double ratio = (cost/sum)*(1.0+0.1*CoinDrand48()); if (ratio<bestRatio) { bestRatio=ratio; bestColumn=iColumn; } } } } if (bestColumn<0) break; // we have finished // Increase chosen column newSolution[bestColumn] += 1.0; double cost = direction * objective[bestColumn]; newSolutionValue += cost; for (CoinBigIndex j=columnStart[bestColumn]; j<columnStart[bestColumn]+columnLength[bestColumn];j++) { int iRow = row[j]; rowActivity[iRow] += element[j]; } }
A solution value of newSolution is compared to the best solution value. If newSolution is an improvement, its feasibility is validated. We expect newSolution to be feasible, and are trapping for unexpected numerical errors.
Example 4.4. Check Solution Quality of newSolution
returnCode=0; // 0 means no good solution if (newSolutionValue<solutionValue) { // minimization // check feasible memset(rowActivity,0,numberRows*sizeof(double)); for (iColumn=0;iColumn<numberColumns;iColumn++) { CoinBigIndex j; double value = newSolution[iColumn]; if (value) { for (j=columnStart[iColumn]; j<columnStart[iColumn]+columnLength[iColumn];j++) { int iRow=row[j]; rowActivity[iRow] += value*element[j]; } } } // check was approximately feasible bool feasible=true; for (iRow=0;iRow<numberRows;iRow++) { if(rowActivity[iRow]<rowLower[iRow]) { if (rowActivity[iRow]<rowLower[iRow]10.0*primalTolerance) feasible = false; } } if (feasible) { // new solution memcpy(betterSolution,newSolution,numberColumns*sizeof(double)); solutionValue = newSolutionValue; // We have good solution returnCode=1; } }
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CBC's concept of branching is based on the idea of an "object". An object has (i) a feasible region, (ii) can be evaluated for infeasibility, (iii) can be branched on, e.g., a method of generating a branching object, which defines an up branch and a down branch, and (iv) allows comparsion of the effect of branching. Instances of objects include.
In Chapter 5, Branching , we give examples of how to use existing branching objects. (The next revision of this Guide should include an example of how to write your own branching object; Contributions of examples are welcome.)
If the user declares variables as integer but does no more, then CBC will treat them as simple integer variables. In many cases the user would like to do some more fine tuning. This section shows how to create integer variables with pseudo costs. When pseudo costs are given then it is assumed that if a variable is at 1.3 then the cost of branching that variable down will be 0.3 times the down pseudo cost and the cost of branching up would be 0.7 times the up pseudo cost. Pseudo costs can be used both for branching and for choosing a node. The full code is in longthin.cpp located in the CBC Samples directory, see Chapter 8, More Samples .
The idea is simple for set covering problems. Branching up gets us much closer to an integer solution so we will encourage that direction by branch up if variable value > 0.333333. The expected cost of going up obviously depends on the cost of the variable. The pseudo costs are choosen to reflect that fact.
Example 5.1. CbcSimpleIntegerPseudoCosts
int iColumn; int numberColumns = solver3>getNumCols(); // do pseudo costs CbcObject ** objects = new CbcObject * [numberColumns]; // Point to objective const double * objective = model.getObjCoefficients(); int numberIntegers=0; for (iColumn=0;iColumn<numberColumns;iColumn++) { if (solver3>isInteger(iColumn)) { double cost = objective[iColumn]; CbcSimpleIntegerPseudoCost * newObject = new CbcSimpleIntegerPseudoCost(&model,numberIntegers,iColumn, 2.0*cost,cost); newObject>setMethod(3); objects[numberIntegers++]= newObject; } } // Now add in objects (they will replace simple integers) model.addObjects(numberIntegers,objects); for (iColumn=0;iColumn<numberIntegers;iColumn++) delete objects[iColumn]; delete [] objects;
The code in Example 5.1 also tries to give more importance to variables with more coefficients. Whether this sort of thing is worthwhile should be the subject of experimentation.
In crew scheduling, the problems are long and thin. A problem may have a few rows but many thousands of variables. Branching a variable to 1 is very powerful as it fixes many other variables to zero, but branching to zero is very weak as thousands of variables can increase from zero. In crew scheduling problems, each constraint is a flight leg, e.g., JFK airport to DFW airport. From DFW there may be several flights the crew could take next  suppose one flight is the 9:30 flight from DFW to LAX airport. A binary branch is that the crew arriving at DFW either take the 9:30 flight to LAX or they don't. This "followon" branching does not fix individual variables. Instead this branching divides all the variables with entries in the JFKDFW constraint into two groups  those with entries in the DFWLAX constraint and those without entries.
The full sample code for followon brancing is in crew.cpp located in the CBC Samples directory, see Chapter 8, More Samples ). In this case, the simple integer variables are left which may be necessary if other sorts of constraints exist. Followon branching rules are to be considered first, so the priorities are set to indicate the followon rules take precedence. Priority 1 is the highest priority.
Example 5.2. CbcFollowOn
int iColumn; int numberColumns = solver3>getNumCols(); /* We are going to add a single followon object but we want to give low priority to existing integers As the default priority is 1000 we don't actually need to give integer priorities but it is here to show how. */ // Normal integer priorities int * priority = new int [numberColumns]; int numberIntegers=0; for (iColumn=0;iColumn<numberColumns;iColumn++) { if (solver3>isInteger(iColumn)) { priority[numberIntegers++]= 100; // low priority } } /* Second parameter is set to true for objects, and false for integers. This indicates integers */ model.passInPriorities(priority,false); delete [] priority; /* Add in objects before we can give them a priority. In this case just one object  but it shows the general method */ CbcObject ** objects = new CbcObject * [1]; objects[0]=new CbcFollowOn(&model); model.addObjects(1,objects); delete objects[0]; delete [] objects; // High priority int followPriority=1; model.passInPriorities(&followPriority,true);
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Table of Contents
CBC uses a generic OsiSolverInterface and its resolve capability. This does not give much flexibility so advanced users can inherit from their interface of choice. This section illustrates how to implement a specialized solver for a long thin problem, e.g., fast0507 again. As with the other examples in the Guide, the sample code is not guaranteed to be the fastest way to solve the problem. The main purpose of the example is to illustrate techniques. The full source is in CbcSolver2.hpp and CbcSolver2.cpp located in the CBC Samples directory, see Chapter 8, More Samples .
The method initialSolve is called a few times in CBC, and provides a convenient starting point. The modelPtr_ derives from OsiClpSolverInterface.
Example 7.1. initialSolve()
// modelPtr_ is of type ClpSimplex * modelPtr_>setLogLevel(1); // switch on a bit of printout modelPtr_>scaling(0); // We don't want scaling for fast0507 setBasis(basis_,modelPtr_); // Put basis into ClpSimplex // Do long thin by sprint ClpSolve options; options.setSolveType(ClpSolve::usePrimalorSprint); options.setPresolveType(ClpSolve::presolveOff); options.setSpecialOption(1,3,15); // Do 15 sprint iterations modelPtr_>initialSolve(options); // solve problem basis_ = getBasis(modelPtr_); // save basis modelPtr_>setLogLevel(0); // switch off printout
The resolve() method is more complicated than initialSolve(). The main pieces of data are a counter count_ which is incremented each solve and an integer array node_ which stores the last time a variable was active in a solution. For the first few solves, the normal Dual Simplex is called and node_ array is updated.
Example 7.2. First Few Solves
if (count_<10) { OsiClpSolverInterface::resolve(); // Normal resolve if (modelPtr_>status()==0) { count_++; // feasible  save any nonzero or basic const double * solution = modelPtr_>primalColumnSolution(); for (int i=0;i<numberColumns;i++) { if (solution[i]>1.0e6modelPtr_>getStatus(i)==ClpSimplex::basic) { node_[i]=CoinMax(count_,node_[i]); howMany_[i]++; } } } else { printf("infeasible early on\n"); } }
After the first few solves, only those variables which took part in a solution in the last so many solves are used. As fast0507 is a set covering problem, any rows which are already covered can be taken out.
Example 7.3. Create Small SubProblem
int * whichRow = new int[numberRows]; // Array to say which rows used int * whichColumn = new int [numberColumns]; // Array to say which columns used int i; const double * lower = modelPtr_>columnLower(); const double * upper = modelPtr_>columnUpper(); setBasis(basis_,modelPtr_); // Set basis int nNewCol=0; // Number of columns in small model // Column copy of matrix const double * element = modelPtr_>matrix()>getElements(); const int * row = modelPtr_>matrix()>getIndices(); const CoinBigIndex * columnStart = modelPtr_>matrix()>getVectorStarts(); const int * columnLength = modelPtr_>matrix()>getVectorLengths(); int * rowActivity = new int[numberRows]; // Number of columns with entries in each row memset(rowActivity,0,numberRows*sizeof(int)); int * rowActivity2 = new int[numberRows]; // Lower bound on row activity for each row memset(rowActivity2,0,numberRows*sizeof(int)); char * mark = (char *) modelPtr_>dualColumnSolution(); // Get some space to mark columns memset(mark,0,numberColumns); for (i=0;i<numberColumns;i++) { bool choose = (node_[i]>count_memory_&&node_[i]>0); // Choose if used recently // Take if used recently or active in some sense if ((choose&&upper[i]) (modelPtr_>getStatus(i)!=ClpSimplex::atLowerBound&& modelPtr_>getStatus(i)!=ClpSimplex::isFixed) lower[i]>0.0) { mark[i]=1; // mark as used whichColumn[nNewCol++]=i; // add to list CoinBigIndex j; double value = upper[i]; if (value) { for (j=columnStart[i]; j<columnStart[i]+columnLength[i];j++) { int iRow=row[j]; assert (element[j]==1.0); rowActivity[iRow] ++; // This variable can cover this row } if (lower[i]>0.0) { for (j=columnStart[i]; j<columnStart[i]+columnLength[i];j++) { int iRow=row[j]; rowActivity2[iRow] ++; // This row redundant } } } } } int nOK=0; // Use to count rows which can be covered int nNewRow=0; // Use to make list of rows needed for (i=0;i<numberRows;i++) { if (rowActivity[i]) nOK++; if (!rowActivity2[i]) whichRow[nNewRow++]=i; // not satisfied else modelPtr_>setRowStatus(i,ClpSimplex::basic); // make slack basic } if (nOK<numberRows) { // The variables we have do not cover rows  see if we can find any that do for (i=0;i<numberColumns;i++) { if (!mark[i]&&upper[i]) { CoinBigIndex j; int good=0; for (j=columnStart[i]; j<columnStart[i]+columnLength[i];j++) { int iRow=row[j]; if (!rowActivity[iRow]) { rowActivity[iRow] ++; good++; } } if (good) { nOK+=good; // This covers  put in list whichColumn[nNewCol++]=i; } } } } delete [] rowActivity; delete [] rowActivity2; if (nOK<numberRows) { // By inspection the problem is infeasible  no need to solve modelPtr_>setProblemStatus(1); delete [] whichRow; delete [] whichColumn; printf("infeasible by inspection\n"); return; } // Now make up a small model with the right rows and columns ClpSimplex * temp = new ClpSimplex(modelPtr_,nNewRow,whichRow,nNewCol,whichColumn);
If the variables cover the rows, then the problem is feasible (no cuts are being used). (If the rows were equality constraints, then this might not be the case. More work would be needed.) After the solution to the subproblem, the reduced costs of the full problem are checked. If the reduced cost of any variable not in the subproblem is negative, the code goes back to the full problem and cleans up with Primal Simplex.
Example 7.4. Check Optimal Solution
temp>setDualObjectiveLimit(1.0e50); // Switch off dual cutoff as problem is restricted temp>dual(); // solve double * solution = modelPtr_>primalColumnSolution(); // put back solution const double * solution2 = temp>primalColumnSolution(); memset(solution,0,numberColumns*sizeof(double)); for (i=0;i<nNewCol;i++) { int iColumn = whichColumn[i]; solution[iColumn]=solution2[i]; modelPtr_>setStatus(iColumn,temp>getStatus(i)); } double * rowSolution = modelPtr_>primalRowSolution(); const double * rowSolution2 = temp>primalRowSolution(); double * dual = modelPtr_>dualRowSolution(); const double * dual2 = temp>dualRowSolution(); memset(dual,0,numberRows*sizeof(double)); for (i=0;i<nNewRow;i++) { int iRow=whichRow[i]; modelPtr_>setRowStatus(iRow,temp>getRowStatus(i)); rowSolution[iRow]=rowSolution2[i]; dual[iRow]=dual2[i]; } // See if optimal double * dj = modelPtr_>dualColumnSolution(); // get reduced cost for large problem // this assumes minimization memcpy(dj,modelPtr_>objective(),numberColumns*sizeof(double)); modelPtr_>transposeTimes(1.0,dual,dj); modelPtr_>setObjectiveValue(temp>objectiveValue()); modelPtr_>setProblemStatus(0); int nBad=0; for (i=0;i<numberColumns;i++) { if (modelPtr_>getStatus(i)==ClpSimplex::atLowerBound &&upper[i]>lower[i]&&dj[i]<1.0e5) nBad++; } // If necessary clean up with primal (and save some statistics) if (nBad) { timesBad_++; modelPtr_>primal(1); iterationsBad_ += modelPtr_>numberIterations(); }
The array node_ is updated, as for the first few solves. To give some idea of the effect of this tactic, the problem fast0507 has 63,009 variables but the small problem never has more than 4,000 variables. In only about ten percent of solves was it necessary to resolve, and then the average number of iterations on full problem was less than 20.
To give another example  again only for illustrative purposes  it is possible to do quadratic MIP with CBC. In this case, we make resolve the same as initialSolve. The full code is in ClpQuadInterface.hpp and ClpQuadInterface.cpp located in the CBC Samples directory, see Chapter 8, More Samples ).
Example 7.5. Solving a Quadratic MIP
// save cutoff double cutoff = modelPtr_>dualObjectiveLimit(); modelPtr_>setDualObjectiveLimit(1.0e50); modelPtr_>scaling(0); modelPtr_>setLogLevel(0); // solve with no objective to get feasible solution setBasis(basis_,modelPtr_); modelPtr_>dual(); basis_ = getBasis(modelPtr_); modelPtr_>setDualObjectiveLimit(cutoff); if (modelPtr_>problemStatus()) return; // problem was infeasible // Now pass in quadratic objective ClpObjective * saveObjective = modelPtr_>objectiveAsObject(); modelPtr_>setObjectivePointer(quadraticObjective_); modelPtr_>primal(); // Th model has a quadratic objective, // so this invokes quadratic primal. modelPtr_>setDualObjectiveLimit(cutoff); if (modelPtr_>objectiveValue()>cutoff) modelPtr_>setProblemStatus(1); modelPtr_>setObjectivePointer(saveObjective);
Rather than implementing all the method from scratch, we based the quadratic solver ClpQuadInteface on the linear programming solver OsiClpSolverInterface. This is a convenient approach to take when prototyping ideas. After the merit of an idea is proven, the user can decide is a more serious implementation is warranted.
Table of Contents
The CBC distribution includes a number of .cpp sample files. Users are encouraged to use them as starting points for their own CBC projects. The files can be found in the COIN/Cbc/Samples/ directory. For the latest information on compiling and running these samples, please see the file COIN/Cbc/Samples/INSTALL. Most of them can be built by
make DRIVER=name
which produces an executable testit. Below is a list of some of the most useful sample files with a short description for each file.
Table 8.1. Basic Samples
Source file  Description 

minimum.cpp  This is a CBC "Hello, world" program. It reads a problem in MPS file format, and solves the problem using simple branchandbound. 
sample2.cpp  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 CGL's preprocess functionality. It uses CbcBranchUser.cpp, CbcCompareUser.cpp and CbcHeuristicUser.cpp with corresponding *.hpp files. 
Table 8.2. Advanced Samples
Source file  Description 

crew.cpp  This sample shows the use of advanced branching and a use of priorities. It uses CbcCompareUser.cpp with corresponding *.hpp files. 
longthin.cpp  This sample shows the advanced use of a solver. It also has coding for a greedy heuristic. The solver is given in CbcSolver2.hpp and CbcSolver2.cpp. The heuristic is given in CbcHeuristicGreedy.hpp and CbcHeuristicGreedy.cpp. It uses CbcBranchUser.cpp and CbcCompareUser.cpp with corresponding *.hpp files. 
qmip.cpp  This solves a quadratic MIP. It is to show advanced use of a solver. The solver is given in ClpQuadInterface.hpp and ClpQuadInterface.cpp. It uses CbcBranchUser.cpp and CbcCompareUser.cpp with corresponding *.hpp files. 
sos.cpp  This artificially creates a Special Ordered Set problem. 
lotsize.cpp  This artificially creates a Lot Sizing problem. 
Messages and codes passed by CBC are listed in the tables below. For a complete list, see COIN/Cbc/CbcMessages.cpp. The notation used is the same as for the printf in the C programming language.
There are several log levels. Setting the log level to be i produces the log messages for level i and all levels less than i.
Table 9.1. CBC Messages Passed At Log Level 0
Code  Text and notes  

3007  No integer variables  nothing to do  

Table 9.2. CBC Messages Passed At or Above Log Level 1
Code  Text and notes  

1  Search completed  best objective %g, took %d iterations and %d nodes  
 
3  Exiting on maximum nodes  
 
4  Integer solution of %g found after %d iterations and %d nodes  
 
5  Partial search  best objective %g (best possible %g), took %d iterations and %d nodes  
 
6  The LP relaxation is infeasible or too expensive  
 
9  Objective coefficients multiple of %g  
 
10  After %d nodes, %d on tree, %g best solution, best possible %g  
 
11  Exiting as integer gap of %g less than %g or %g%%  
 
12  Integer solution of %g found by heuristic after %d iterations and %d nodes  
 
13  At root node, %d cuts changed objective from %g to %g in %d passes  
 
14  Cut generator %d (%s)  %d row cuts (%d active), %d column cuts %? in %g seconds  new frequency is %d  
 
16  Integer solution of %g found by strong branching after %d iterations and %d nodes  
 
17  %d solved, %d variables fixed, %d tightened  
 
18  After tightenVubs, %d variables fixed, %d tightened  
 
19  Exiting on maximum solutions  
 
20  Exiting on maximum time  
 
23  Cutoff set to %g  equivalent to best solution of %g  
 
24  Integer solution of %g found by subtree after %d iterations and %d nodes  
 
26  Setting priorities for objects %d to %d inclusive (out of %d)  
 
3008  Strong branching is fixing too many variables, too expensively!  

Q:.  What is CBC? 
A:.  The COINOR Branch and Cut code is designed to be a high quality mixed integer code provided under the terms of the Common Public License. CBC is written in C++, and is primarily intended to be used as a callable library (though a rudimentary standalone executable exists). The first documented release was .90.0 The current release is version .90.0. (JF 04/01/05) 
Q:.  What are some of the features of CBC? 
A:.  CBC allows the use of any CGL cuts and the use of heuristics and specialized branching methods. (JF 04/01/05) 
Q:.  How do I obtain and install CBC? 
A:.  Please see the COINOR FAQ for details on how to obtain and install COINOR modules. (JF 04/01/05) 
Q:.  Is CBC reliable? 
A:.  CBC has been tested on many problems, but more testing and improvement is needed before it can get to version 1.0. (JF 04/01/05) 
Q:.  Is there any documentation for CBC? 
A:.  If you can see this you have the best there is:) Also available is a list of CBC class descriptions generated by Doxygen. (JF 04/01/05) 
Q:.  Is CBC as fast as Cplex or Xpress? 
A:.  No. However its design is much more flexible so advanced users will be able to tailor CBC to their needs. (JF 04/01/05) 
Q:.  When will version 1.0 of CBC be available? 
A:.  It is expected that version 1.0 will be released in time for the 2005 INFORMS annual meeting. (JF 04/01/05) 
Q:.  What can the community do to help? 
A:.  People from all around the world are already helping. There are probably ten people who do not always post to the discussion mail list but are constantly "improving" the code by demanding performance or bug fixes or enhancements. And there are others posting questions to discussion groups. (JF 04/01/05) A good start is to join the coindiscuss mailing list where CBC is discussed. Some other possibilities include:

There is Doxygen content for CBC available online at http://www.coinor.org/Doxygen/Cbc/index.html. A local version of the Doxygen content can be generated from the CBC distribution. To do so, in the directory COIN/Cbc, enter make doc. The Doxygen content will be created in the directory COIN/Cbc/Doc/html. The same can be done for the COIN core, from the COIN/Coin directory.
Revision History  

Revision 0.21  May 10, 2005  RLH 
Fixed typos caught by Cole Smith, editor of the INFORMS Tutorial Book, and added place holders for needstobewritten sections, e.g., Using CGL with CBC.  
Revision 0.2  May 2, 2005  RLH 
Book chapter for CBC Tutorial at INFORMS 2005 annual meeting. Reorganized the content. Added CBC Messages. Changed the font type to distinguish functions/variables/classnames/code from text.  
Revision 0.1  April 1, 2005  JF 
First draft. The CBC documentation uses the DocBook CLP documentation created by David de la Nuez. 