The LP Procedure |
In this example, the HALDI10 problem from Example 5.10 is solved. However, here the default strategy for searching the branch-and-bound tree is modified. By default, the search strategy has VARSELECT=FAR. This means that when searching for an integer variable on which to branch, the procedure uses the one that has a value farthest from an integer value. An alternative strategy has VARSELECT=PENALTY. This strategy causes PROC LP to look at the cost, in terms of the objective function, of branching on an integer variable. The procedure looks at PENALTYDEPTH= integer variables before choosing the one with the largest cost. This is a much more expensive strategy (in terms of execution time) than the VARSELECT=FAR strategy, but it can be beneficial if fewer integer iterations must be done to find an optimal solution.
proc lp data=haldi10 varselect=penalty; run;
Compare the number of integer iterations needed to solve the problem using this heuristic with the default strategy used in Example 5.10. In this example, the difference is profound; in general, solution times can vary significantly with the search technique. See Output 5.11.1.
Problem Summary | |
---|---|
Objective Function | Max _OBS1_ |
Rhs Variable | _rhs_ |
Type Variable | _type_ |
Problem Density (%) | 31.82 |
Variables | Number |
Integer | 6 |
Binary | 6 |
Slack | 10 |
Total | 22 |
Constraints | Number |
LE | 10 |
Objective | 1 |
Total | 11 |
Integer Iteration Log | ||||||||
---|---|---|---|---|---|---|---|---|
Iter | Problem | Condition | Objective | Branched | Value | Sinfeas | Active | Proximity |
1 | 0 | ACTIVE | 18.709524 | x4 | 0.8 | 1.11905 | 2 | . |
2 | 1 | ACTIVE | 16.585187 | x1 | 0.447 | 2.33824 | 3 | . |
3 | 2 | ACTIVE | 14.86157 | x5 | 0.221 | 2.09584 | 4 | . |
4 | 3 | ACTIVE | 14.807195 | x2 | 0.897 | 1.31729 | 5 | . |
5 | -4 | ACTIVE | 14.753205 | x8 | 14.58 | 0.61538 | 6 | . |
6 | 5 | ACTIVE | 14.730078 | x6 | 0.043 | 0.79446 | 7 | . |
7 | -6 | ACTIVE | 13.755102 | x3 | 0.051 | 0.58163 | 8 | . |
8 | -7 | ACTIVE | 11.6 | x8 | 11.6 | 0.4 | 9 | . |
9 | 8 | ACTIVE | 11.6 | x12 | 0.6 | 0.4 | 10 | . |
10 | -9 | ACTIVE | 11.6 | x8 | 10.6 | 0.4 | 11 | . |
11 | 10 | ACTIVE | 11.6 | x12 | 1.6 | 0.4 | 12 | . |
12 | -11 | ACTIVE | 11.6 | x8 | 9.6 | 0.4 | 13 | . |
13 | 12 | ACTIVE | 11.6 | x12 | 2.6 | 0.4 | 14 | . |
14 | -13 | ACTIVE | 11.571429 | x9 | 0.143 | 0.57143 | 15 | . |
15 | 14 | ACTIVE | 11.5 | x8 | 8.5 | 0.5 | 16 | . |
16 | -15 | INFEASIBLE | 9 | . | . | . | 15 | . |
17 | 15 | ACTIVE | 11.375 | x12 | 3.375 | 0.375 | 16 | . |
18 | -17 | ACTIVE | 11.166667 | x8 | 7.167 | 0.16667 | 17 | . |
19 | 18 | ACTIVE | 11.125 | x12 | 4.125 | 0.125 | 18 | . |
20 | 19 | SUBOPTIMAL | 11 | . | . | . | 7 | 7 |
21 | 7 | ACTIVE | 13.5 | x8 | 13.5 | 0.5 | 8 | 7 |
22 | -21 | INFEASIBLE | 11 | . | . | . | 7 | 7 |
23 | 21 | ACTIVE | 13.375 | x12 | 0.375 | 0.375 | 8 | 7 |
24 | -23 | ACTIVE | 13.166667 | x8 | 12.17 | 0.16667 | 9 | 7 |
25 | 24 | ACTIVE | 13.125 | x12 | 1.125 | 0.125 | 10 | 7 |
26 | 25 | SUBOPTIMAL | 13 | . | . | . | 4 | 5 |
27 | 6 | ACTIVE | 14.535714 | x3 | 0.045 | 0.50893 | 5 | 5 |
28 | -27 | FATHOMED | 12.625 | . | . | . | 4 | 5 |
29 | 27 | SUBOPTIMAL | 14 | . | . | . | 1 | 4 |
30 | -1 | ACTIVE | 18.309524 | x3 | 0.129 | 1.31905 | 2 | 4 |
31 | 30 | ACTIVE | 17.67723 | x6 | 0.886 | 0.43662 | 3 | 4 |
32 | 31 | ACTIVE | 15.485156 | x2 | 0.777 | 1.50833 | 4 | 4 |
33 | -32 | ACTIVE | 15.2625 | x1 | 0.121 | 1.38333 | 4 | 4 |
34 | 33 | ACTIVE | 15.085106 | x10 | 3.532 | 0.91489 | 4 | 4 |
35 | 34 | FATHOMED | 14.857143 | . | . | . | 3 | 4 |
36 | 32 | FATHOMED | 11.212121 | . | . | . | 2 | 4 |
37 | -31 | ACTIVE | 17.56338 | x10 | 7.93 | 0.43662 | 3 | 4 |
38 | 37 | ACTIVE | 17.225962 | x8 | 2.38 | 0.69231 | 4 | 4 |
39 | 38 | ACTIVE | 17.221818 | x1 | 0.016 | 0.37111 | 5 | 4 |
40 | -39 | FATHOMED | 14.43662 | . | . | . | 4 | 4 |
41 | 39 | ACTIVE | 17.172375 | x2 | 0.133 | 0.31948 | 5 | 4 |
42 | 41 | ACTIVE | 16.890196 | x5 | 0.086 | 0.19608 | 6 | 4 |
43 | 42 | ACTIVE | 16.75 | x12 | 9.75 | 0.25 | 7 | 4 |
44 | -43 | SUBOPTIMAL | 15 | . | . | . | 6 | 3 |
45 | 43 | SUBOPTIMAL | 16 | . | . | . | 3 | 2 |
46 | -38 | FATHOMED | 17.138028 | . | . | . | 2 | 2 |
47 | -37 | SUBOPTIMAL | 17 | . | . | . | 1 | 1 |
48 | -30 | FATHOMED | 16.566667 | . | . | . | 0 | . |
Solution Summary | |
---|---|
Integer Optimal Solution |
|
Objective Value | 17 |
Phase 1 Iterations | 0 |
Phase 2 Iterations | 13 |
Phase 3 Iterations | 79 |
Integer Iterations | 48 |
Integer Solutions | 6 |
Initial Basic Feasible Variables | 12 |
Time Used (seconds) | 0 |
Number of Inversions | 17 |
Epsilon | 1E-8 |
Infinity | 1.797693E308 |
Maximum Phase 1 Iterations | 100 |
Maximum Phase 2 Iterations | 100 |
Maximum Phase 3 Iterations | 99999999 |
Maximum Integer Iterations | 100 |
Time Limit (seconds) | 120 |
Variable Summary | ||||||
---|---|---|---|---|---|---|
Col | Variable Name | Status | Type | Price | Activity | Reduced Cost |
1 | x1 | DEGEN | BINARY | 0 | 0 | 0 |
2 | x2 | BINARY | 0 | 0 | -4 | |
3 | x3 | BINARY | 0 | 0 | -4 | |
4 | x4 | BINARY | 0 | 1 | -18 | |
5 | x5 | DEGEN | BINARY | 0 | 0 | 0 |
6 | x6 | BINARY | 0 | 1 | -1 | |
7 | x7 | INTEGER | 1 | 0 | -6.5 | |
8 | x8 | INTEGER | 1 | 0 | -3 | |
9 | x9 | INTEGER | 1 | 0 | -1 | |
10 | x10 | INTEGER | 1 | 8 | -8 | |
11 | x11 | INTEGER | 1 | 0 | -8.545455 | |
12 | x12 | BASIC | INTEGER | 1 | 9 | 0 |
13 | _OBS2_ | BASIC | SLACK | 0 | 20 | 0 |
14 | _OBS3_ | BASIC | SLACK | 0 | 5 | 0 |
15 | _OBS4_ | BASIC | SLACK | 0 | 10 | 0 |
16 | _OBS5_ | SLACK | 0 | 0 | -1 | |
17 | _OBS6_ | SLACK | 0 | 0 | -1.5 | |
18 | _OBS7_ | DEGEN | SLACK | 0 | 0 | 0 |
19 | _OBS8_ | DEGEN | SLACK | 0 | 0 | 0 |
20 | _OBS9_ | BASIC | SLACK | 0 | 2 | 0 |
21 | _OBS10_ | SLACK | 0 | 0 | -2.545455 | |
22 | _OBS11_ | BASIC | SLACK | 0 | 2 | 0 |
Constraint Summary | ||||||
---|---|---|---|---|---|---|
Row | Constraint Name | Type | S/S Col | Rhs | Activity | Dual Activity |
1 | _OBS1_ | OBJECTVE | . | 0 | 17 | . |
2 | _OBS2_ | LE | 13 | 110 | 90 | 0 |
3 | _OBS3_ | LE | 14 | 95 | 90 | 0 |
4 | _OBS4_ | LE | 15 | 80 | 70 | 0 |
5 | _OBS5_ | LE | 16 | 100 | 100 | 1 |
6 | _OBS6_ | LE | 17 | 0 | 0 | 1.5 |
7 | _OBS7_ | LE | 18 | 0 | 0 | 0 |
8 | _OBS8_ | LE | 19 | 0 | 0 | 0 |
9 | _OBS9_ | LE | 20 | 0 | -2 | 0 |
10 | _OBS10_ | LE | 21 | 0 | 0 | 2.5454545 |
11 | _OBS11_ | LE | 22 | 0 | -2 | 0 |
Although the VARSELECT=PENALTY strategy works well in this example, there is no guarantee that it will work well with your model. Experimentation with various strategies is necessary to find the one that works well with your model and data, particularly if a model is solved repeatedly with few changes to either the structure or the data.
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