
Mixed integer formulations using natural variables for single machine scheduling around a common due date
While almost all existing works which optimally solve justintime sched...
read it

Branchandcut algorithms for the covering salesman problem
The Covering Salesman Problem (CSP) is a generalization of the Traveling...
read it

Valid inequalities and a branchandcut algorithm for the routing and spectrum allocation problem
One of the most promising solutions to deal with huge data traffic deman...
read it

An Exact Approach for the Balanced kWay Partitioning Problem with Weight Constraints and its Application to Sports Team Realignment
In this work a balanced kway partitioning problem with weight constrain...
read it

Finding Minimal Cost Herbrand Models with BranchCutandPrice
Given (1) a set of clauses T in some firstorder language L and (2) a c...
read it

Learning Combined Set Covering and Traveling Salesman Problem
The Traveling Salesman Problem is one of the most intensively studied co...
read it

Bayesian Network Learning via Topological Order
We propose a mixed integer programming (MIP) model and iterative algorit...
read it
MIP and Set Covering approaches for Sparse Approximation
The Sparse Approximation problem asks to find a solution x such that y  Hx < α, for a given norm ·, minimizing the size of the support x_0 := #{j  x_j ≠ 0 }. We present valid inequalities for Mixed Integer Programming (MIP) formulations for this problem and we show that these families are sufficient to describe the set of feasible supports. This leads to a reformulation of the problem as an Integer Programming (IP) model which in turn represents a Minimum Set Covering formulation, thus yielding many families of valid inequalities which may be used to strengthen the models up. We propose algorithms to solve sparse approximation problems including a branch & cut for the MIP, a twostages algorithm to tackle the set covering IP and a heuristic approach based on Local Branching type constraints. These methods are compared in a computational experimentation with the goal of testing their practical potential.
READ FULL TEXT
Comments
There are no comments yet.