Package com.gpudb.protocol
Class MatchGraphRequest.SolveMethod
java.lang.Object
com.gpudb.protocol.MatchGraphRequest.SolveMethod
Enclosing class:
A set of string constants for the
MatchGraphRequest parameter solveMethod.
The type of solver to use for graph matching.
Field Summary
FieldsModifier and TypeFieldDescriptionstatic final StringMatchessamplePointsto the graph using the Hidden Markov Model (HMM)-based method, which conducts a range-tree closest-edge search to find the best combinations of possible road segments (NUM_SEGMENTS) for each sample point to create the best route.static final StringMatchessamplePointssource and destination pairs for the shortest path solves in batch mode.static final StringMatches an optimal path across a number of ev-charging stations between source and target locations.static final StringMatches the graph nodes with a cluster index using Louvain clustering algorithm.static final StringCreates vector node embeddings.static final StringSolves for isochrones for a set of input sources.static final StringMatches closed loops (Eulerian paths) originating and ending at each graph node within min and max hops (levels).static final StringMatchessamplePointsto find the most probable path between origin and destination pairs with cost constraints.static final StringMatches a pattern in the graph.static final StringMatches the pickups and dropoffs by optimizing the total trip costs.static final StringComputes detour costs for nearby stations at a mark point along each source-target route.static final StringMatches the intersection set(s) by computing the Jaccard similarity score between node pairs.static final StringMatchessamplePointsto optimize scheduling multiple supplies (trucks) with varying sizes to varying demand sites with varying capacities per depot.
Field Details
MARKOV_CHAIN
MatchessamplePointsto the graph using the Hidden Markov Model (HMM)-based method, which conducts a range-tree closest-edge search to find the best combinations of possible road segments (NUM_SEGMENTS) for each sample point to create the best route. The route is secured one point at a time while looking aheadCHAIN_WIDTHnumber of points, so the prediction is corrected after each point. This solution type is the most accurate but also the most computationally intensive. Related options:NUM_SEGMENTSandCHAIN_WIDTH.See Also:MATCH_OD_PAIRS
MatchessamplePointsto find the most probable path between origin and destination pairs with cost constraints.See Also:MATCH_SUPPLY_DEMAND
MatchessamplePointsto optimize scheduling multiple supplies (trucks) with varying sizes to varying demand sites with varying capacities per depot. Related options:PARTIAL_LOADINGandMAX_COMBINATIONS.See Also:MATCH_BATCH_SOLVES
MatchessamplePointssource and destination pairs for the shortest path solves in batch mode.See Also: