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We describe a class of constraint satisfaction problems (CSPs) for which a global solution can be found by a fast parallel algorithm.
We enumerate a collection of 1, 2, 3, and 4-arity gates/cogates that represent constraints, and define a class of constraints that is possible under the assumption of a "bridge" between two particular basis changes.
Second, AI and robotics researchers can both benefit from the provided theoretical guarantees of system stability on a class of constraint-balancing tasks that occur in very large action spaces.
We assume that we have a class of constraints C with some reasonable computational and closure properties (the computational properties of interest are that the satisfiability problem and an appropriate version of the variable elimination problem for C should be solvable in PTIME).
While the optimal DR-planning problem is NP-hard even for (general) 2D bar joint constraint systems, we describe an O(n3) algorithm for a broad class of constraint systems that are isostatic or underconstrained.
Our class of constraints is chosen near-optimal as already minor extensions of its expressiveness cause potential intractability.
To address these deficiencies, we propose a new class of constraint-aggregation method that we call induced aggregates.
In the paper we consider bounds on differences (which are an important class of constraints based on linear inequalities) and we analyze the computational complexity of query answering.
These principal components maximize variation subject to smoothness and orthogonality constraints; but we allow for a general class of constraints and multivariate densities, including densities without compact support and even densities with algebraic tails.
In the following three subsections, we elaborate on each class of constraints to systematize their physical meanings and relations.
We compare tractable classes of constraint satisfaction problems (CSPs).
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