Abstract—Structural learning is finding the best network that fits the available data and is optimally complex. This can be accomplished by utilizing a search algorithm over the possible network structures. In this paper, a greater importance is given to the search algorithm because we have assumed

We show that exhaustive search and local search need not differ fundamentally but in many cases are variations of the same search strategy. In particular, many local search and GRASP-like procedures have essentially the same structure as branch-and-bound methods, while tabu and other heuristic

game theory values. The program does not use an analytical approach, instead it relies on exhaustive search in a bottom-up strategy. Here it is applied on all positions on game boards which fit into an underlying game board of size 11 by 2. The results show that even under these restrictions Amazons

or after detailed placement, it approximately doubles the improvement in wirelength. Our contributions are based on exhaustive search for functional symmetries in sub-circuits consisting of several gates. Our graphbased symmetry finding is more comprehensive than previously known algorithms — it detects

complementarity alone and involves an exhaustive search of all possible docking conformations in the 6-dimensional space of translations and rotations. Using the barnase/barstar protein complex (1BRS) as a test case, we explore the additional input parameters such as the number of allowed overlaps and the optimal

Abstract. In RSA equation: ed = k · φ(N) + 1, we may guess on par-tial bits of d or p+ q by doing an exhaustive search to further extend the security boundary of d. In this paper, we discuss the following question: Does guessing on p + q bring more benefit than guessing on d? We pro

In this paper we present a method for finding all expansive polynomials with a prescribed degree n and constant term c0. Our research is motivated by the fact that expansivity is a necessary condition for number system constructions. We use the algorithm for an exhaustive search of CNS polynomials

, exhaustive searches are limited to short sequences. We discuss an exhaustive search algorithm with run time characteristic O(1.85N) and apply it to compile a table of exact groundstates of the Bernasconimodel up to N = 48. The data suggests F> 9 for the optimal merit factor in the limit N → ∞. 1

Abstract. Perhaps surprisingly, there are infinite sets that admit mechanical exhaustive search in finite time. We investigate three related questions: What kinds of infinite sets admit mechanical exhaustive search in finite time? How do we systematically build such sets? How fast can exhaustive

this paper we consider only exhaustive searching techniques rather than more radical ones such as genetic algorithms, simulated annealing or the like. Among search algorithms, we consider only backtracking methods to which the cardinality of the constraints is irrelevant. This narrowing of our