This discussion of the advanced presolve interface begins with a quick introduction to presolve. Most of the information in this section will be familiar to people who are interested in the advanced interface, but everyone is encouraged to read through it nonetheless.

As most CPLEX users know, presolve is a process whereby the problem input by the user is examined for logical reduction opportunities. The goal is to reduce the size of the problem passed to the requested optimizer. A reduction in problem size typically translates to a reduction in total run time (even including the time spent in presolve itself).

Consider scorpion.mps from NETLIB as an example:

CPLEX> disp pr st
Problem name: scorpion.mps
Constraints        :     388  [Less: 48,  Greater: 60,  Equal: 280]
Variables          :     358
Constraint nonzeros:    1426
Objective  nonzeros:     282
RHS        nonzeros:      76
CPLEX> optimize
Tried aggregator 1 time.
LP Presolve eliminated 138 rows and 82 columns.
Aggregator did 193 substitutions.
Reduced LP has 57 rows, 83 columns, and 327 nonzeros.
Presolve time =    0.00 sec.
 
Iteration log . . .
Iteration:     1   Dual objective     =           317.965093
 
Dual - Optimal:  Objective =    1.8781248227e+03
Solution time =    0.01 sec.  Iterations = 54 (0)

CPLEX is presented with a problem with 388 constraints and 358 variables, and after presolve the dual simplex method is presented with a problem with 57 constraints and 83 variables. Dual simplex solves this problem and passes the solution back to the presolve routine, which then unpresolves the solution to produce a solution to the original problem. During this process, presolve builds an entirely new `presolved' problem and stores enough information to translate a solution to this problem back to a solution to the original problem. This information is hidden within the user's problem (in the CPLEX LP problem object, for Callable Library users) and was inaccessible to the user in CPLEX releases prior to 7.0.