[Scip] variable stats

[Gregor Hendel]

Dear James,

I am afraid you found a function of SCIP where the documentation is 
incomplete.

The branch statistics contain the following variable information;

prio: The branch priority of this variable. Only the branching 
candidates with highest priority are branched on at a node
factor: The branching factor, which is a softer way to prefer certain 
variables by multiplying their actual score function.
..
depth: The average branching depth of a variable
branchings up/down: The actual number of times this variable has been 
branched on in the respective directions.
sb: the number of times that this variable has been subject to the 
strong branching look-ahead method.

Inferences up/down: The average number of domain reductions to other 
variables observed after branching on this variables.

Cutoffs up/down: The percentage of infeasible subproblems obtained after 
branching on this variable in the respective direction

LP Gain down/up: The average unit gain observed in the objective 
function of the child node LP relaxations after branching on this 
variable in the respective direction. For the unit gain, you measure the 
actual, nonnegative gain of the LP objective of the child node ccompared 
to its parent node, and normalize it by the variable fractionality. 
Example: A variable with LP relax solution value 0.3 has been branched 
on and we (later, or directly via strong branching) observed a gain of 
+3 in the LP objective of the down child node, this is a unit gain of 3 
/ 0.3 = 10 in the down direction.

pscostcount down/up: How many LP unit gains have been observed for this 
variable?

Note that the latter number can be quite different from the actual 
number of branchings for various reasons, when the created child nodes 
were pruned or cut off without observing an actual gain.

Kind regards,
Gregor



Am 26.10.2014 um 20:24 schrieb James Cussens:
> Hi folks,
>
> I don't understand all of the information that is provided when SCP
> prints out statistics on variables after solving. This sort of thing:
>
> variable          prio   factor   down     up  depth    down      up
>   sb     down       up   down     up      down        up    down
> up
> t_I#0                0      1.0      5      6   77.2       9      28
>    0      3.6     25.4   0.0%   0.0%   62.9177  101.0077     0.0
> 0.0
> ....
>
> Could someone tell me where to find an explanation of this? Apologies
> if this has been dealt with before.
>
> James
>