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<div class="moz-cite-prefix">Hi Samuel and list,<br>
<br>
Am 18.02.2016 um 14:02 schrieb Deleplanque Samuel:<br>
</div>
<blockquote
cite="mid:CAAO5AhBJYE2g7kiK4hVj0F+eR9Gp1gJJbXU8xexs1uUsDzde6g@mail.gmail.com"
type="cite">
<div dir="ltr">Dear SCIP team,
<div><br>
</div>
<div>I'm working on a typical pricing problem. I've two
formulations: one is solved with a Branch & Price and the
other one with a typical Branch & Bound, both using SCIP.</div>
<div>I have a problem with collecting the LP Root for the second
one. Indeed, I would like the LP-Relaxation objective value of
my problem and not the transformed one, or any other solution
obtained in the root node.</div>
</div>
</blockquote>
You should use SCIPretransformObjval(scip, transformedobjval) to
transform objective values from the transformed space back to the
original problem space. By convention, we call values in the
transformed space the lower and upper bound and their original
counterparts dual and primal bound, respectively.<br>
<br>
In order to obtain the first LP value, you can use the special
function SCIPgetFirstLPDualboundRoot(scip).<br>
<br>
<blockquote
cite="mid:CAAO5AhBJYE2g7kiK4hVj0F+eR9Gp1gJJbXU8xexs1uUsDzde6g@mail.gmail.com"
type="cite">
<div dir="ltr">
<div><br>
</div>
<div>For example, in the trace bellow, the LP-Relaxation
objective value for an instance of my formulation is
1.041834e+04 (the MIP optimal is found before any branching).</div>
<div><br>
</div>
<div> time | node | left |LP iter|LP it/n| mem |mdpt |frac
|vars |cons |cols |rows |cuts |confs|strbr| dualbound |
primalbound | gap <br>
</div>
<div>
<div>T 0.0s| 1 | 0 | 0 | - | 248k| 0 | - |
10 | 20 | 10 | 20 | 0 | 0 | 0 | -- |
0.000000e+00 | Inf </div>
<div> 0.0s| 1 | 0 | 13 | - | 246k| 0 | 2 |
10 | 20 | 10 | 20 | 0 | 0 | 0 | 1.041834e+04 |
0.000000e+00 | Inf </div>
<div>s 0.0s| 1 | 0 | 13 | - | 247k| 0 | 2 |
10 | 20 | 10 | 20 | 0 | 0 | 0 | 1.041834e+04 |
3.029395e+03 | 243.91%</div>
<div> 0.0s| 1 | 0 | 16 | - | 248k| 0 | 0 |
10 | 20 | 10 | 21 | 1 | 0 | 0 | 9.957000e+03 |
3.029395e+03 | 228.68%</div>
<div>* 0.0s| 1 | 0 | 16 | - | 248k| 0 | - |
10 | 20 | 10 | 21 | 1 | 0 | 0 | 9.957000e+03 |
9.957000e+03 | 0.00%</div>
</div>
<div><br>
</div>
<div>If I use any function to obtain the LPRoot value (<i>SCIPgetLPRootObjval</i>,
<i>SCIPgetLPRootObjval</i> or <i>SCIPgetLowerboundRoot</i>) I
logically get 9.957000e+03. If I use <i>SCIPgetSolOrigObj</i>
I obtain the same and not the 1.041834e+04 of the LP
relaxation of my original problem.</div>
<div><br>
</div>
<div>Also, because I want to compare the two formulations, and
then to compare what is comparable, I would like to know what
are the different algorithms executed in the root. If I try to
understand: the T and s mean two heuristics has been used and
* means normally an integral LP-relaxation (but not for my
original problem).</div>
</div>
</blockquote>
Exactly. Use 'display statistics' or SCIPprintStatistics() to print
an exhaustive plugin summary to see which of the plugins ran, which
succeeded, and which wasted your precious user time. All these
statistics are also available through the API, but I would suggest
to have a look at the tabular output first to get an impression of
what is important.<br>
<br>
A list of the primal heuristics of SCIP and their display characters
is available with the command 'display heuristics'.<br>
<br>
Kind regards,<br>
Gregor<br>
<br>
<blockquote
cite="mid:CAAO5AhBJYE2g7kiK4hVj0F+eR9Gp1gJJbXU8xexs1uUsDzde6g@mail.gmail.com"
type="cite">
<div dir="ltr">
<div><br>
</div>
<div>Thanks :)</div>
<div><br>
</div>
<div>Samuel Deleplanque</div>
<div><br>
</div>
<div>-- <br>
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<div style="font-family:arial"><font size="1"><span
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