RUI: Strategies for Fast Execution of Constraint Logic Programs Over a Real-Number Domain

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Constraint logic programming is a declarative programming paradigm which has proven to be expressive for computationally difficult problems such as scheduling, air traffic flow management, simulation of dynamic physical systems, solution of differential equations, and options trading analysis. However, while CLP languages are expressive, their execution is not always efficient enough for the problems to which they apply. The goal of this research is to develop strategies for fast execution of constraint logic programs, particularly in the domain of real or rational arithmetic constraints. To this end, incremental Gaussian elimination-based and simplex-based constraint satisfaction algorithms are compared for their efficiency in the context of CLP; the feasibility of intelligent backtracking and independent and-parallelism is investigated; work on asynchronous parallel execution of the inference engine and solver is continued; and parallel incremental algorithms for constraint solving are being developed. The research program has strong undergraduate involvement and research components. Students are involved in the design, implementation, and testing of algorithms, and are exposed to concepts from the theory of computation, programming language design, the linear programming problem, AI search and backtracking strategies, and parallel computation.