Name
Abstract | ||
|---|---|---|
This paper discusses a new method to perform propagation over a (two-layer, feed-forward) Neural Network embedded in a Constraint Programming model. The method is meant to be employed in Empirical Model Learning, a technique designed to enable optimal decision making over systems that cannot be modeled via conventional declarative means. The key step in Empirical Model Learning is to embed a Machine Learning model into a combinatorial model. It has been showed that Neural Networks can be embedded in a Constraint Programming model by simply encoding each neuron as a global constraint, which is then propagated individually. Unfortunately, this decomposition approach may lead to weak bounds. To overcome such limitation, we propose a new network-level propagator based on a non-linear Lagrangian relaxation that is solved with a subgradient algorithm. The method proved capable of dramatically reducing the search tree size on a thermal-aware dispatching problem on multicore CPUs. The overhead for optimizing the Lagrangian multipliers is kept within a reasonable level via a few simple techniques. This paper is an extended version of [], featuring an improved structure, a new filtering technique for the network inputs, a set of overhead reduction techniques, and a thorough experimentation. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1007/s10601-015-9234-6 | Constraints |
Keywords | DocType | Volume |
Constraint programming,Lagrangian relaxation,Neural networks | Journal | 21 |
Issue | ISSN | Citations |
4 | 1383-7133 | 3 |
PageRank | References | Authors |
0.35 | 20 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Michele Lombardi | 1 | 270 | 28.86 |
| Stefano Gualandi | 2 | 156 | 15.95 |