Incremental Updates of Generalized Hypertree Decompositions
This addresses an incremental improvement for researchers and practitioners in constraint solving, reducing computational overhead when CSPs are modified.
The paper tackles the problem of updating generalized hypertree decompositions (GHDs) for constraint satisfaction problems (CSPs) when the CSP changes slightly, proposing a framework that avoids recomputing decompositions from scratch. Experimental results indicate practical applicability, though no concrete numbers are provided.
Structural decomposition methods, such as generalized hypertree decompositions, have been successfully used for solving constraint satisfaction problems (CSPs). As decompositions can be reused to solve CSPs with the same constraint scopes, investing resources in computing good decompositions is beneficial, even though the computation itself is hard. Unfortunately, current methods need to compute a completely new decomposition even if the scopes change only slightly. In this paper, we make the first steps toward solving the problem of updating the decomposition of a CSP $P$ so that it becomes a valid decomposition of a new CSP $P'$ produced by some modification of $P$. Even though the problem is hard in theory, we propose and implement a framework for effectively updating GHDs. The experimental evaluation of our algorithm strongly suggests practical applicability.