backjumping造句

• Backjumping at internal nodes cannot be done as for leaf nodes.
• It uses the same rationale of graph-based backjumping.
• The backjumping algorithm by Gaschnig does a backjump only in leaf dead ends.
• The efficiency of a backjumping algorithm depends on how high it is able to backjump.
• In practice, backjumping algorithms use the lowest index they can efficiently prove to be a safe jump.
• Jumpback learning is based on storing as constraints the inconsistent assignments that would be found by conflict-based backjumping.
• The simplest condition in which backjumping is possible is when all values of a variable have been proved inconsistent without further branching.
• The fact that nodes skipped by backjumping can be ignored when considering a further backjump can be exploited by the following algorithm.
• Since sets of variables are only sent when retracing from nodes, the sets collected at nodes skipped by backjumping are automatically ignored.
• In particular, the algorithm collects, in each node, all sets coming from its descendants that have not been skipped by backjumping.
• When retracting from this node, this set is removed the variable of the node and collected into the destination of backtracking or backjumping.
• For this reason, conflict-based backjumping orders constraints in such a way constraints over lower indices variables are preferred over constraints on higher index variables.
• His most notable contribution is his invention of Conflict-directed backjumping, an advanced technique for reducing search in constraint problems by avoiding unnecessary work on backtracking.
• When retracting from a leaf node, the set of variables that are in constraint with it is created and " sent back " to its parent, or ancestor in case of backjumping.
• When further backtracking or backjumping from the node, the variable of the node is removed from this set, and the set is sent to the node that is the destination of backtracking or backjumping.
• When further backtracking or backjumping from the node, the variable of the node is removed from this set, and the set is sent to the node that is the destination of backtracking or backjumping.
• A still more refined backjumping algorithm, sometimes able to achieve larger backjumps, is based on checking not only the common presence of two variables in the same constraint but also on whether the constraint actually caused inconsistency.
• A hybrid approach can be taken by using variable elimination for creating the new constraints that are propagated within nodes, and a search algorithm ( such as backtracking, backjumping, local search ) on each individual node.
• In constraint satisfaction, a "'hybrid algorithm "'solves a constraint satisfaction problem by the combination of two different methods, for example variable conditioning ( backtracking, backjumping, etc . ) and constraint inference ( arc consistency, variable elimination, etc .)
• Conflict-driven solvers augment the basic DPLL search algorithm with efficient conflict analysis, clause learning, non-chronological backtracking ( a . k . a . backjumping ), as well as " two-watched-literals " unit propagation, adaptive branching, and random restarts.