Probabilistic constructive optimization techniques

We have developed a new optimization paradigm for solving computationally intractable combinatorial optimization and synthesis problems. The technique, named probabilistic constructive, combines the advantages of both constructive and probabilistic optimization mechanisms. Since it is a constructive approach, it has a relatively short runtime and is amenable for the inclusion of insights through heuristic rules. The probabilistic nature facilitates a flexible tradeoff between runtime and the quality of solution, suitability for the superimposition of a variety of control strategies, and simplicity of implementation. After presenting the generic technique, we apply it to a generic NP-complete problem (maximum independent set) and a synthesis and compilation problem (sequential code covering). Extensive experimentation indicates that the new approach provides very attractive tradeoffs between the quality of solution and runtime, often outperforming the best previously published approaches.