NAG FL Interface
h02bbf (ilp_​dense)

Note: this routine is deprecated. Replaced by h02bkf.
A modern branch and bound algorithm that uses the NAG optimization modelling suite (see Section 3.1 in the E04 Chapter Introduction) has been introduced. This new routine h02bkf has access to all of the suite facilities and uses the same interface as all compatible solvers.
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1 Purpose

h02bbf solves ‘zero-one’, ‘general’, ‘mixed’ or ‘all’ integer programming problems using a branch and bound method. The routine may also be used to find either the first integer solution or the optimum integer solution. It is not intended for large sparse problems.

2 Specification

Fortran Interface
Subroutine h02bbf ( itmax, msglvl, n, m, a, lda, bl, bu, intvar, cvec, maxnod, intfst, maxdpt, toliv, tolfes, bigbnd, x, objmip, iwork, liwork, rwork, lrwork, ifail)
Integer, Intent (In) :: msglvl, n, m, lda, intvar(n), maxnod, intfst, maxdpt, liwork, lrwork
Integer, Intent (Inout) :: itmax, ifail
Integer, Intent (Out) :: iwork(liwork)
Real (Kind=nag_wp), Intent (In) :: a(lda,*), bl(n+m), bu(n+m), cvec(n)
Real (Kind=nag_wp), Intent (Inout) :: toliv, tolfes, bigbnd, x(n)
Real (Kind=nag_wp), Intent (Out) :: objmip, rwork(lrwork)
C Header Interface
#include <nag.h>
void  h02bbf_ (Integer *itmax, const Integer *msglvl, const Integer *n, const Integer *m, const double a[], const Integer *lda, const double bl[], const double bu[], const Integer intvar[], const double cvec[], const Integer *maxnod, const Integer *intfst, const Integer *maxdpt, double *toliv, double *tolfes, double *bigbnd, double x[], double *objmip, Integer iwork[], const Integer *liwork, double rwork[], const Integer *lrwork, Integer *ifail)
The routine may be called by the names h02bbf or nagf_mip_ilp_dense.

3 Description

h02bbf is capable of solving certain types of integer programming (IP) problems using a branch and bound (B&B) method, see Taha (1987). In order to describe these types of integer programs and to briefly state the B&B method, we define the following Linear Programming (LP) problem:
Minimize
F(x)=c1x1+c2x2++cnxn  
subject to
j= 1naijxj { = } bi,   i= 1,2,,m  
ljxjuj,  j=1,2,,n (1)
If, in (1), it is required that (some or) all the variables take integer values, then the integer program is of type (mixed or) all general IP problem. If additionally, the integer variables are restricted to take only 01 values (i.e., lj=0 and uj=1) then the integer program is of type (mixed or all) zero-one IP problem.
The B&B method applies directly to these integer programs. The general idea of B&B (for a full description see Dakin (1965) or Mitra (1973)) is to solve the problem without the integral restrictions as an LP problem (first node). If in the optimal solution an integer variable xk takes a noninteger value xk*, two LP sub-problems are created by branching, imposing xk[xk*] and xk[xk*]+1 respectively, where [xk*] denotes the integer part of xk*. This method of branching continues until the first integer solution (bound) is obtained. The hanging nodes are then solved and investigated in order to prove the optimality of the solution. At each node, an LP problem is solved using e04mff/​e04mfa.

4 References

Dakin R J (1965) A tree search algorithm for mixed integer programming problems Comput. J. 8 250–255
Mitra G (1973) Investigation of some branch and bound strategies for the solution of mixed integer linear programs Math. Programming 4 155–170
Taha H A (1987) Operations Research: An Introduction Macmillan, New York

5 Arguments

1: itmax Integer Input/Output
On entry: an upper bound on the number of iterations for each LP problem.
On exit: unchanged if on entry itmax>0, else itmax=max(50,5×(n+m)).
2: msglvl Integer Input
On entry: the amount of printout produced by h02bbf, as indicated below (see Section 5.1 for a description of the printed output). All output is written to the current advisory message unit (as defined by x04abf).
Value Definition
0 No output.
1 The final IP solution only.
5 One line of output for each node investigated and the final IP solution.
10 The original LP solution (first node), one line of output for each node investigated and the final IP solution.
3: n Integer Input
On entry: n, the number of variables.
Constraint: n>0.
4: m Integer Input
On entry: m, the number of general linear constraints.
Constraint: m0.
5: a(lda,*) Real (Kind=nag_wp) array Input
Note: the second dimension of the array a must be at least n if m>0 and at least 1 if m=0.
On entry: the ith row of a must contain the coefficients of the ith general constraint, for i=1,2,,m.
If m=0 then the array a is not referenced.
6: lda Integer Input
On entry: the first dimension of the array a as declared in the (sub)program from which h02bbf is called.
Constraint: ldamax(1,m).
7: bl(n+m) Real (Kind=nag_wp) array Input
8: bu(n+m) Real (Kind=nag_wp) array Input
On entry: bl must contain the lower bounds and bu the upper bounds, for all the constraints in the following order. The first n elements of each array must contain the bounds on the variables, and the next m elements the bounds for the general linear constraints (if any). To specify a nonexistent lower bound (i.e., lj=-), set bl(j)-bigbnd and to specify a nonexistent upper bound (i.e., uj=+), set bu(j)bigbnd. To specify the jth constraint as an equality, set bl(j)=bu(j)=β, say, where |β|<bigbnd.
Constraints:
  • bl(j)bu(j), for j=1,2,,n+m;
  • if bl(j)=bu(j)=β, |β|<bigbnd.
9: intvar(n) Integer array Input
On entry: indicates which are the integer variables in the problem. For example, if xj is an integer variable then intvar(j) must be set to 1, and 0 otherwise.
Constraints:
  • intvar(j)=0 or 1, for j=1,2,,n;
  • intvar(j)=1 for at least one value of j.
10: cvec(n) Real (Kind=nag_wp) array Input
On entry: the coefficients cj of the objective function F(x)=c1x1+c2x2++cnxn. The routine attempts to find a minimum of F(x). If a maximum of F(x) is desired, cvec(j) should be set to -cj, for j=1,2,,n, so that the routine will find a minimum of -F(x).
11: maxnod Integer Input
On entry: the maximum number of nodes that are to be searched in order to find a solution (optimum integer solution). If maxnod0 and intfst0, then the B&B tree search is continued until all the nodes have been investigated.
12: intfst Integer Input
On entry: specifies whether to terminate the B&B tree search after the first integer solution (if any) is obtained. If intfst>0 then the B&B tree search is terminated at node k say, which contains the first integer solution. For maxnod>0 this applies only if kmaxnod.
13: maxdpt Integer Input
On entry: the maximum depth of the B&B tree used for branch and bound.
Suggested value: maxdpt=3×n/2.
Constraint: maxdpt2.
14: toliv Real (Kind=nag_wp) Input/Output
On entry: the integer feasibility tolerance; i.e., an integer variable is considered to take an integer value if its violation does not exceed toliv. For example, if the integer variable xj is near unity then xj is considered to be integer only if (1-toliv)xj(1+toliv).
On exit: unchanged if on entry toliv>0.0, else toliv=10−5.
15: tolfes Real (Kind=nag_wp) Input/Output
On entry: the maximum acceptable absolute violation in each constraint at a ‘feasible’ point (feasibility tolerance); i.e., a constraint is considered satisfied if its violation does not exceed tolfes.
On exit: unchanged if on entry tolfes>0.0, else tolfes=ε (where ε is the machine precision).
16: bigbnd Real (Kind=nag_wp) Input/Output
On entry: the ‘infinite’ bound size in the definition of the problem constraints. More precisely, any upper bound greater than or equal to bigbnd will be regarded as + and any lower bound less than or equal to -bigbnd will be regarded as -.
On exit: unchanged if on entry bigbnd>0.0, else bigbnd=1020.
17: x(n) Real (Kind=nag_wp) array Input/Output
On entry: an initial estimate of the original LP solution.
On exit: with ifail=0, x contains a solution which will be an estimate of either the optimum integer solution or the first integer solution, depending on the value of intfst. If ifail=9, then x contains a solution which will be an estimate of the best integer solution that was obtained by searching maxnod nodes.
18: objmip Real (Kind=nag_wp) Output
On exit: with ifail=0 or 9, objmip contains the value of the objective function for the IP solution.
19: iwork(liwork) Integer array Communication Array
20: liwork Integer Input
On entry: the dimension of the array iwork as declared in the (sub)program from which h02bbf is called.
Constraint: liwork(25+n+m)×maxdpt+5×n+m+4.
21: rwork(lrwork) Real (Kind=nag_wp) array Communication Array
22: lrwork Integer Input
On entry: the dimension of the array rwork as declared in the (sub)program from which h02bbf is called.
Constraint: lrworkmaxdpt×(n+1)+2× min(n,m+1) 2 +14×n+12×m.
If msglvl>0, the amounts of workspace provided and required (with maxdpt=3×n/2) are printed. As an alternative to computing maxdpt, liwork and lrwork from the formulas given above, you may prefer to obtain appropriate values from the output of a preliminary run with the values of maxdpt, liwork and lrwork set to 1. If however only liwork and lrwork are set to 1, then the appropriate values of these arguments for the given value of maxdpt will be computed and printed unless maxdpt<2. In both cases h02bbf will then terminate with ifail=6.
23: ifail Integer Input/Output
On entry: ifail must be set to 0, −1 or 1 to set behaviour on detection of an error; these values have no effect when no error is detected.
A value of 0 causes the printing of an error message and program execution will be halted; otherwise program execution continues. A value of −1 means that an error message is printed while a value of 1 means that it is not.
If halting is not appropriate, the value −1 or 1 is recommended. If message printing is undesirable, then the value 1 is recommended. Otherwise, the value −1 is recommended since useful values can be provided in some output arguments even when ifail0 on exit. When the value -1 or 1 is used it is essential to test the value of ifail on exit.
On exit: ifail=0 unless the routine detects an error or a warning has been flagged (see Section 6).

5.1 Description of Printed Output

The level of printed output from h02bbf is controlled by you (see the description of msglvl in Section 5).
When msglvl>0, the summary printout at the end of execution of h02bbf includes a listing of the status of every variable and constraint. Note that default names are assigned to all variables and constraints. The following describes the printout for each variable.
Varbl gives the name (V) and index j, for j=1,2,,n, of the variable.
State gives the state of the variable (FR if neither bound is in the working set, EQ if a fixed variable, LL if on its lower bound, UL if on its upper bound, TF if temporarily fixed at its current value). If Value lies outside the upper or lower bounds by more than the Feasibility Tolerance, State will be ++ or -- respectively.
Value is the value of the variable at the final iterate.
Lower Bound is the lower bound specified for the variable. (None indicates that bl(j)-bigbnd.) Note that if intvar(j)=1, then the printed value of Lower Bound for the jth variable may not be the same as that originally supplied in bl(j).
Upper Bound is the upper bound specified for the variable. (None indicates that bu(j)bigbnd.) Note that if intvar(j)=1, then the printed value of Upper Bound for the jth variable may not be the same as that originally supplied in bu(j).
Lagr Mult is the value of the Lagrange-multiplier for the associated bound constraint. This will be zero if State is FR or TF. If x is optimal, the multiplier should be non-negative if State is LL, and non-positive if State is UL.
Residual is the difference between the variable Value and the nearer of its bounds bl(j) and bu(j).
The meaning of the printout for general constraints is the same as that given above for variables, with ‘variable’ replaced by ‘constraint’, bl(j) and bu(j) are replaced by bl(n+j) and bu(n+j) respectively, and with the following change in the heading.
L Con gives the name (L) and index j, for j=1,2,,m, of the constraint.
When msglvl>1, the summary printout at the end of every node during the execution of h02bbf is a listing of the outcome of forcing an integer variable with a noninteger value to take a value within its specified lower and upper bounds.
Node No is the current node number of the B&B tree being investigated.
Parent Node is the parent node number of the current node.
Obj Value is the final objective function value. If a node does not have a feasible solution then No Feas Soln is printed instead of the objective function value. If a node whose optimum solution exceeds the best integer solution so far is encountered (i.e., it does not pay to explore the sub-problem any further), then its objective function value is printed together with a CO (Cut Off).
Varbl Chosen is the index of the integer variable chosen for branching.
Value Before is the noninteger value of the integer variable chosen.
Lower Bound is the lower bound value that the integer variable is allowed to take.
Upper Bound is the upper bound value that the integer variable is allowed to take.
Value After is the value of the integer variable after the current optimization.
Depth is the depth of the B&B tree at the current node.

6 Error Indicators and Warnings

If on entry ifail=0 or −1, explanatory error messages are output on the current error message unit (as defined by x04aaf).
Errors or warnings detected by the routine:
Note: in some cases h02bbf may return useful information.
ifail=1
The problem does not have a feasible integer solution.
It was not possible to satisfy all the integer variables to within the integer feasibility tolerance (determined by toliv). Increase toliv and rerun h02bbf.
ifail=2
The LP solution is unbounded.
This value of ifail implies that a step as large as bigbnd would have to be taken in order to continue the algorithm (see Section 9).
ifail=3
The LP does not have a feasible solution.
It was not possible to satisfy all the constraints to within the feasibility tolerance (determined by tolfes). If the data for the constraints are accurate only to the absolute precision σ, you should ensure that the value of the feasibility tolerance is greater than σ. For example, if all elements of A are of order unity and are accurate only to three decimal places, the feasibility tolerance should be at least 10-3 (see Section 9).
ifail=4
Iteration limit (determined by itmax) reached without finding a solution. (See Section 9.)
ifail=6
On entry, i=value, intvar(i)=value.
Constraint: intvar=0 or 1.
On entry, lda=value and m=value.
Constraint: ldamax(1,m).
On entry, m=value.
Constraint: m0.
On entry, maxdpt=value.
Constraint: maxdpt2.
On entry, n=value.
Constraint: n>0.
On entry, the dimension of iwork is too small: liwork=value. iwork must be of dimension (at least) value.
On entry, the dimension of rwork is too small: lrwork=value. rwork must be of dimension (at least) value.
On entry, there were value infinite or inconsistent bounds given in arrays bl and bu. For further advisory details set msglvl>2.
ifail=7
Search of a branch was terminated due to iteration limit.
The IP solution reported is not the optimum IP solution. In other words, the B&B tree search for at least one of the branches had to be terminated since an LP sub-problem in the branch did not have a solution (see Section 9).
ifail=8
maxdpt is too small to solve the problem: maxdpt=value.
Increase maxdpt and rerun h02bbf.
ifail=9
The IP solution returned is the best solution for the number of nodes investigated in the B&B tree.
ifail=10
No feasible solution was found for the number of nodes investigated in the B&B tree.
ifail=11
Not enough workspace to solve problem.
Although the workspace sizes are sufficient to meet the documented restriction, they are not sufficiently large to accommodate an internal partition of the workspace that meets the requirements of the problem. Increase the workspace sizes.
The maximum depth of the B&B tree used for branch and bound (determined by maxdpt) is too small. Increase maxdpt (along with liwork and/or lrwork if appropriate) and rerun h02bbf.
Overflow
It may be possible to avoid the difficulty by increasing the magnitude of the feasibility tolerance (tolfes) and rerunning the program. If the message recurs even after this change, see Section 9.
ifail=-99
An unexpected error has been triggered by this routine. Please contact NAG.
See Section 7 in the Introduction to the NAG Library FL Interface for further information.
ifail=-399
Your licence key may have expired or may not have been installed correctly.
See Section 8 in the Introduction to the NAG Library FL Interface for further information.
ifail=-999
Dynamic memory allocation failed.
See Section 9 in the Introduction to the NAG Library FL Interface for further information.

7 Accuracy

h02bbf implements a numerically stable active set strategy and returns solutions that are as accurate as the condition of the problem warrants on the machine.

8 Parallelism and Performance

Background information to multithreading can be found in the Multithreading documentation.
h02bbf is not thread safe and should not be called from a multithreaded user program. Please see Section 1 in FL Interface Multithreading for more information on thread safety.
h02bbf makes calls to BLAS and/or LAPACK routines, which may be threaded within the vendor library used by this implementation. Consult the documentation for the vendor library for further information.
Please consult the X06 Chapter Introduction for information on how to control and interrogate the OpenMP environment used within this routine. Please also consult the Users' Note for your implementation for any additional implementation-specific information.

9 Further Comments

The original LP problem may not have an optimum solution, i.e., h02bbf terminates with ifail=2, 3 or 4 or overflow may occur. In this case, you are recommended to relax the integer restrictions of the problem and try to find the optimum LP solution by using e04mff/​e04mfa instead.
In the B&B method, it is possible for an LP sub-problem to terminate without finding a solution. This may occur due to the number of iterations exceeding the maximum allowed. Therefore, the B&B tree search for that particular branch cannot be continued. Thus the returned solution may not be optimal. (ifail=7). For the second and unlikely case, a solution could not be found despite a second attempt at an LP solution.

10 Example

This example solves the integer programming problem:
maximize
F(x)=3x1+4x2  
subject to the bounds
x1 0 x2 0  
and to the general constraints
2x1+5x215 2x1-2x25 3x1+2x25  
where x1 and x2 are integer variables.
The initial point, which is feasible, is
x0= (1,1) T,  
and F(x0)=7.
The optimal solution is
x*= (2,2) T,  
and F(x*)=14.
Note that maximizing F(x) is equivalent to minimizing -F(x).

10.1 Program Text

Program Text (h02bbfe.f90)

10.2 Program Data

Program Data (h02bbfe.d)

10.3 Program Results

Program Results (h02bbfe.r)