Column generation, combined with an appropriate integer programming technique, has shown to be a powerful tool for solving huge integer programs arising in various applications. In these column generation approaches, the master problem is often of a set partitioning type. The set partitioning polytope has the quasi-integrality property, which enables the use of simplex pivots for finding improv...

Column generation refers to linear programming (LP) algorithms designed to solve problems in which there are a huge number of variables compared to the number of constraints and the simplex algorithm step of determining whether the current basic solution is optimal or finding a variable to enter the basis is done by solving an optimization problem rather than by enumeration. To the best of my k...

We introduce a novel algorithm for temporal planning in Golog using shared resources, and describe the Bulk Freight Rail Scheduling Problem, a motivating example of such a temporal domain. We use the framework of column generation to tackle complex resource constrained temporal planning problems that are beyond the scope of current planning technology by combining: the global view of a linear p...

We consider the following basic rostering problem. The days are divided in three fixed shifts (Early, Late, Night) and for each day (seven days a week) a number of personnel required is given. To man these shifts, a given number of employees are available, who all have their individual preferences. We have to find a year-roster for each person with minimum total cost. This cost consists of two ...

In this paper we deal with the problem of building a timetable for the courses of a university faculty. We propose an integer linear programming approach based on column generation. Each column is associated to a weekly timetable of a single course. The constraints referring to classroom occupancy and non overlapping in time of courses are in the integer linear programming matrix. The constrain...

Fast nearest neighbor searching is becoming an increasingly important tool in solving many large-scale problems. Recently a number of approaches to learning datadependent hash functions have been developed. In this work, we propose a column generation based method for learning datadependent hash functions on the basis of proximity comparison information. Given a set of triplets that encode the ...

We examine linear program (LP) approaches to boosting and demonstrate their efficient solution using LPBoost, a column generation simplex method. We prove that minimizing the soft margin error function (equivalent to solving an LP) directly optimizes a generalization error bound. LPBoost can be used to solve any boosting LP by iteratively optimizing the dual classification costs in a restricted...

Airline recovery presents very large and difficult problems requiring high quality solutions within very short time limits. To improve computational performance, various solution approaches have been employed, including decomposition methods and approximation techniques. There has been increasing interest in the development of efficient and accurate solution techniques to solve an integrated ai...