Deep learning frameworks optimize the computation graphs and intra-operator computations to boost inference performance on GPUs, while inter-operator parallelism is usually ignored. In this paper, a unified framework, AutoGraph, proposed obtain highly optimized in favor of parallel executions GPU kernels. A novel dynamic programming algorithm, combined with backtracking search, adopted explore ...

The min-conflict heuristic (Minton et al. 1992) has been introduced into backtracking algorithms and iterative improvement algorithms as a powerful heuristic for solving constraint satisfaction problems. Backtracking algorithms become inefficient when a bad partial solution is constructed, since an exhaustive search is required for revising the bad decision. On the other hand, iterative improve...

Considering the theoretical hardness of SAT, the astonishing adeptness of SAT solvers when attacking practical problems has changed the way we perceive the limits of algorithmic reasoning. Modern SAT solvers are based on the idea of conflict driven clause learning (CDCL). The CDCL algorithm is a combination of an explicit backtracking search for a satisfying assignment complemented with a deduc...

The only technique available for solving many important problems is searching. Since searching can be extremely costly, it is important to identify features that improve the efficiency of aearch algorithms. We compute the efficiency of simple backtracking, of an algorithm similar to backtracking except that it notices when the predicate is empty instead of noticing when it is unsatlsfiable (the...

The min-con ict heuristic (Minton et al. 1992) has been introduced into backtracking algorithms and iterative improvement algorithms as a powerful heuristic for solving constraint satisfaction problems. Backtracking algorithms become ine cient when a bad partial solution is constructed, since an exhaustive search is required for revising the bad decision. On the other hand, iterative improvemen...

In this dissertation we investigate theoretical aspects of some practical approaches used in solving and understanding search problems. We concentrate on the Satisfiability problem, which is a strong representative from search problem domains. The work develops general theoretical foundations to investigate some practical aspects of satisfiability search. This results in a better understanding ...

protection systems have vital role in network reliability in short circuit mode and proper operating for relays. current transformer often in transient and saturation under short circuit mode causes mal-operation of relays which will have undesirable effects. therefore, proper and quick identification of current transformer saturation is so important. in this paper, an artificial neural network...

Many combinatorial problems can be modeled as 0/1 integer linear programs. Problems expressed in this form are usually solved by Operations Research algorithms, but good results have also been obtained using generalised SAT algorithms based on backtracking or local search, after transformation to pseudo-Boolean form. A third class of SAT algorithm uses non-systematic backtracking to combine con...

Efficient utilization of time and effort is essential in Personnel scheduling problems to evenly balance the workload among the people and attempt to satisfy the personnel preferences. In Constraint Satisfaction Problem based scheduling problems, when a branch of the search fails the backtracking search algorithm back up to the preceding variable and try a different value for it. So here the mo...

In this paper we introduce an Ant Colony Optimisation (ACO) algorithm to find solutions for the well-known Knight’s Tour problem. The algorithm utilizes the implicit parallelism of ACO’s to simultaneously search for tours starting from all positions on the chessboard. We compare the new algorithm to a recently reported genetic algorithm, and to a depth-first backtracking search using Warnsdorff...