Discrete Optimization, Graphs & Networks

Discrete Optimization is a branch of applied mathematics which has its roots in Combinatorics and Graph Theory. Discrete Optimization is located on the boundary between mathematics and computer science where it touches algorithm theory and computational complexity. It has important applications in several fields, including artificial intelligence, mathematics, and software engineering. As opposed to continuous optimization, the variables used in the mathematical program (or some of them) are restricted to assume only discrete values, such as the integers. Two notable branches of discrete optimization are: combinatorial optimization,which refers to problems on graphs, matroids and other discrete structures, and integer programming. These branches are closely intertwined however since many combinatorial optimization problems can be modeled as integer programs and, conversely, integer programs can often be given a combinatorial interpretation. Graphs and networks play an important role in modelling problems; they appear naturally in various applications and there is a variety of efficient algorithms available for solving "standard" problems on networks. However, many problems in Discrete Optimization are "difficult" to solve (for instance NP-hard).  Thus, especially for problems of practical interest one would like to compromise on the quality of the solution for the sake of computing a suboptimal solution quickly.

We solicit contributions to original research from this spectrum of topics, theoretical, empirical and practical papers,  including models and computational results.