Graph Neural Networks for Maximum Constraint Satisfaction

Solving Constraint Satisfaction Problems Using Neural Networks

Maximum Constraint Satisfaction on Diamonds

In this paper we study the complexity of the (weighted) maximum constraint satisfaction problem (Max CSP) over an arbitrary finite domain. In this problem, one is given a collection of weighted constraints on overlapping sets of variables, and the goal is to find an assignment of values to the variables so as to maximize the total weight of satisfied constraints. Max Cut is a typical example of...

Approximation for Maximum Surjective Constraint Satisfaction Problems

Maximum surjective constraint satisfaction problems (Max-SurCSPs) are computational problems where we are given a set of variables denoting values from a finite domain B and a set of constraints on the variables. A solution to such a problem is a surjective mapping from the set of variables to B such that the number of satisfied constraints is maximized. We study the approximation performance t...

Evolutionary Neural Networks for Value Ordering in Constraint Satisfaction Problems

A new method for developing good value-ordering strategies in constraint satisfaction search is presented. Using an evolutionary technique called SANE, in which individual neurons evolve to cooperate and form a neural network, problem-speci c knowledge can be discovered that results in better value-ordering decisions than those based on problem-general heuristics. A neural network was evolved i...

Training Recurrent Neural Networks as a Constraint Satisfaction Problem

This paper presents a new approach for training artificial neural networks using techniques for solving the constraint satisfaction problem (CSP). The quotient gradient system (QGS) is a trajectory based method for solving the CSP. This study converts the training set of a neural network into a CSP and uses the QGS to find its solutions. The QGS finds the global minimum of the optimization prob...