Attempt to fix variables by bounding reduced costs. More...
#include <CoinPresolveDual.hpp>
Public Member Functions | |
remove_dual_action (int nactions, const CoinPresolveAction *next) | |
Static Public Member Functions | |
static const CoinPresolveAction * | presolve (CoinPresolveMatrix *prob, const CoinPresolveAction *next) |
Attempt to fix variables by bounding reduced costs. |
Attempt to fix variables by bounding reduced costs.
The reduced cost of x_j is d_j = c_j - y*a_j (1). Assume minimization, so that at optimality d_j >= 0 for x_j nonbasic at lower bound, and d_j <= 0 for x_j nonbasic at upper bound.
For a slack variable s_i, c_(n+i) = 0 and a_(n+i) is a unit vector, hence d_(n+i) = -y_i. If s_i has a finite lower bound and no upper bound, we must have y_i <= 0 at optimality. Similarly, if s_i has no lower bound and a finite upper bound, we must have y_i >= 0.
For a singleton variable x_j, d_j = c_j - y_i*a_ij. Given x_j with a single finite bound, we can bound d_j greater or less than 0 at optimality, and that allows us to calculate an upper or lower bound on y_i (depending on the bound on d_j and the sign of a_ij).
Now we have bounds on some subset of the y_i, and we can use these to calculate upper and lower bounds on the d_j, using bound propagation on (1). If we can manage to bound some d_j as strictly positive or strictly negative, then at optimality the corresponding variable must be nonbasic at its lower or upper bound, respectively. If the required bound is lacking, the problem is unbounded.
There is no postsolve object specific to remove_dual_action, but execution will queue postsolve actions for any variables that are fixed.
Definition at line 36 of file CoinPresolveDual.hpp.
remove_dual_action::remove_dual_action | ( | int | nactions, | |
const CoinPresolveAction * | next | |||
) |
static const CoinPresolveAction* remove_dual_action::presolve | ( | CoinPresolveMatrix * | prob, | |
const CoinPresolveAction * | next | |||
) | [static] |
Attempt to fix variables by bounding reduced costs.
Always scans all variables. Propagates bounds on reduced costs until there's no change or until some set of variables can be fixed.