AIFeb 13, 2013

Topological Parameters for Time-Space Tradeoff

arXiv:1302.3573v1112 citations
AI Analysis

This work addresses the time-space tradeoff problem for researchers and practitioners in probabilistic and deterministic networks, but it appears incremental as it builds on existing tree-clustering and conditioning methods.

The paper tackles the problem of selecting algorithms that trade space for time in reasoning and optimization tasks by proposing a family of algorithms combining tree-clustering with conditioning, enabling selection based on problem structure to meet specific time-space specifications.

In this paper we propose a family of algorithms combining tree-clustering with conditioning that trade space for time. Such algorithms are useful for reasoning in probabilistic and deterministic networks as well as for accomplishing optimization tasks. By analyzing the problem structure it will be possible to select from a spectrum the algorithm that best meets a given time-space specification.

Foundations

The foundational work for this paper's niche, ranked by how specifically the neighbourhood builds on it — not by global fame.

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