Choosing the right representation for a problem is important. In this article we introduce a linear genetic programming approach for motif discovery in protein families, and we al...
We introduce a new approach to approximating weighted Max-SAT problems that is based on simplifying a given instance, and then tightening the approximation. First, we relax its str...
Abstract. In recent years we have witnessed the proliferation of semidefinite programming bounds in combinatorial optimization [1,5,8], quantum computing [9,2,3,6,4,16] and even in...
Creating good integer programming formulations had, as a basic axiom, the rule “Find formulations with tighter linear relaxations”. This rule, while useful when using unsophist...
Learning from ambiguous training data is highly relevant in many applications. We present a new learning algorithm for classification problems where labels are associated with se...