Avoiding redundant columns by adding classical Benders cuts to column generation subproblems

Combinatorial Benders' Cuts

Constraint Programming Based Column Generation with Knapsack Subproblems

Approximate Linear Programming for Network Control: Column Generation and Subproblems

Approximate linear programming (ALP) has been shown to provide useful bounds on optimal average cost for multiclass queueing network control problems. Approximation architectures can be chosen so that a manageable number of variables gives moderate accuracy; however, the number of constraints grows exponentially in the number of queues. We use column generation to more efficiently solve the dua...

Inexact Cuts in Benders Decomposition

Benders' decomposition is a well-known technique for solving large linear programs with a special structure. In particular it is a popular technique for solving multi-stage stochastic linear programming problems. Early termination in the subproblems generated during Benders' decomposition (assuming dual feasibility) produces valid cuts which are inexact in the sense that they are not as constra...

Regularization by Adding Redundant Features

The Pseudo Fisher Linear Discriminant (PFLD) based on a pseudo-inverse technique shows a peaking behaviour of the generalization error for training sample sizes that are about the feature size: with an increase in the training sample size the generalization error at first decreases reaching the minimum, then increases reaching the maximum at the point where the training sample size is equal to ...