Continuous Representation Techniques in Combinatorial Optimization

Polyhedral techniques in combinatorial optimization

Generally, combinatorial optimization problems are easy to formulate, but hard to solve. The most successfull approaches, cutting plane algorithms and column generation, rely on the (mixed) integer linear programming formulation of a problem. The theory of polyhedra, i.e., polyhedral combinatorics, is the foundation of these techniques. This manuscript intends to give an overview of polyhedral ...

P : Polyhedral techniques in combinatorial optimization

Polyhedral Techniques in Combinatorial Optimization II: Computations

The polyhedral approach is one of the most powerful techniques available for solving hard combinatorial optimization problems. The main idea behind the technique is to consider the linear relaxation of the integer combinatorial optimization problem, and try to iteratively strengthen the linear formulation by adding violated strong valid inequalities, i.e., inequalities that are violated by the ...

Polyhedral Techniques in Combinatorial Optimization I: Theory

Combinatorial optimization problems appear in many disciplines ranging from management and logistics to mathematics, physics, and chemistry. These problems are usually relatively easy to formulate mathematically, but most of them are computationally hard due to the restriction that a subset of the variables have to take integral values. During the last two decades there has been a remarkable pr...

Neural techniques for combinatorial optimization with applications

After more than a decade of research, there now exist several neural-network techniques for solving NP-hard combinatorial optimization problems. Hopfield networks and self-organizing maps are the two main categories into which most of the approaches can be divided. Criticism of these approaches includes the tendency of the Hopfield network to produce infeasible solutions, and the lack of genera...