3260 papers • 126 benchmarks • 313 datasets
The Steiner tree problem is a computational problem in computer science and graph theory that involves finding the minimum weight subgraph in an undirected graph that connects a given set of terminal vertices. The goal of the Steiner tree problem is to minimize the total weight of the edges in the subgraph, and it is considered NP-hard, meaning that finding the optimal solution is computationally difficult.
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This paper considers finding one solution to instances of answer set programming (ASP), which is a logic-based declarative modeling and solving framework, and describes the underlying concepts of the implementation, which is based on a novel approach that is called multi-pass dynamic programming (M-DPSINC).
The Dijkstra-Steiner algorithm is enhanced and a revisited algorithm, called DS* is established, which allows for arbitrary lower bounds as heuristics relaxing the previous condition on the heuristic function, and can now use linear programming based lower bounds.
This paper considers the recently popular beyond-worst-case algorithm analysis model which integrates machine-learned predictions with online algorithm design and considers the online Steiner tree problem in this model for both directed and undirected graphs.
This paper designs discretization methods by leveraging the unique characteristics of the Steiner tree, and proposes new training schemes for handling the dynamic Steiner points emerging during the incremental construction.
This paper studies a prototype problem called query-decision regression with task shifts, focusing on the shortest path problem and the minimum Steiner tree problem, and provides theoretical insights regarding the design of realizable hypothesis spaces for building scoring models and presents two principled learning frameworks.
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