Optimization bounds from the branching dual

We present a general method for obtaining strong bounds for discrete optimization problems that is based on a concept of branching duality. It can be applied when no useful integer programming model is available, and we illustrate this with the minimum bandwidth problem. The method strengthens a known bound for a given problem by formulating a dual problem whose feasible solutions are partial branching trees. It solves the dual problem with a “worst-bound” local search heuristic that explores neighboring partial trees. After proving some optimality properties of the heuristic, we show that it substantially improves known combinatorial bounds for the minimum bandwidth problem with a modest amount of computation. It also obtains significantly tighter bounds than depth-first and breadth-first branching, demonstrating that the dual perspective can lead to better branching strategies when the object is to find valid bounds.Accepted manuscrip

A partial breadth-first execution model for prolog

MEM (Multipath Execution Model) is a novel model for the execution of Prolog programs which combines a depth-first and breadth-first exploration of the search tree. The breadth-first search allows more than one path of the SLD-tree to be explored at the same time. In this way, the computational cost of traversing the whole search tree associated to a program can be decreased because the MEM model reduces the overhead due to the execution of control instructions and also diminishes the number of unifications to be performed. This paper focuses on the description of the MEM model and its sequential implementation. Moreover, the MEM execution model can be implemented in order to exploit a new kind of parallelism, called path parallelism, which allows the parallel execution of unify operations related to simultaneously traversed pathsPeer ReviewedPostprint (published version

Specialization effect and its influence on memory and problem solving in expert chess players

Expert chess players, specialized in different openings, recalled positions and solved problems within and outside their area of specialization. While their general expertise was at a similar level players performed better with stimuli from their area of specialization. The effect of specialization on both recall and problem solving was strong enough to override general expertise – players remembering positions and solving problems from their area of specialization performed at around the level of players one standard deviation above them in general skill. Their problem solving strategy also changed depending on whether the problem was within their area of specialization or not. When it was, they searched more in depth and less in breadth; with problems outside their area of specialization, the reverse. The knowledge that comes from familiarity with a problem area is more important than general purpose strategies in determining how an expert will tackle it. These results demonstrate the link in experts between problem solving and memory of specific experiences and indicate that the search for context independent general purpose problem solving strategies to teach to future experts is unlikely to be successful