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Relational linear programming
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DOI：10.1016/j.artint.2015.06.009.png)
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Abstract
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We propose relational linear programming, a simple framework for combining linear programs (LPs) and logic programs. A relational linear program (RLP) is a declarative LP template defining the objective and the constraints through the logical concepts of objects, relations, and quantified variables. This allows one to express the LP objective and constraints relationally for a varying number of individuals and relations among them without enumerating them. Together with a logical knowledge base, effectively a logic program consisting of logical facts and rules, it induces a ground LP. This ground LP is solved using lifted linear programming. That is, symmetries within the ground LP are employed to reduce its dimensionality, if possible, and the reduced program is solved using any off-the-shelf LP solver. In contrast to mainstream LP template languages such as AMPL, which features a mixture of declarative and imperative programming styles, RLP's relational nature allows a more intuitive representation of optimization problems, in particular over relational domains. We illustrate this empirically by experiments on approximate inference in Markov logic networks using LP relaxations, on solving Markov decision processes, and on collective inference using LP support vector machines. (C) 2015 Elsevier B.V. All rights reserved.
Keywords:
Machine learning
Optimization
Relational logic
Statistical relational learning
Linear programming
Symmetry
(Fractional) automorphism
Color-refinement
Lifted probabilistic inference
Lifted linear programming
Equitable partitions
Orbit partitions
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