Abstract: We present a method to design parallel algorithms for constrained combinatorial optimization problems. Our method solves and generalizes many classical combinatorial optimization problems including the stable marriage problem, the shortest path problem and the market clearing price problem. These three problems are solved in the literature using Gale-Shapley algorithm, Dijkstra’s algorithm, and Demange, Gale, Sotomayor algorithm. Our method solves all these problems by casting them as searching for an element that satisfies an appropriate predicate in a distributive lattice. Moreover, it solves generalizations of all these problems — namely finding the optimal solution satisfying additional constraints called lattice-linear predicates. For stable marriage problems, an example of such a constraint is that Peter’s regret is less than that of Paul. For shortest path problems, an example of such a constraint is that cost of reaching vertex v1 is at least the cost of reaching vertex v2. For the market clearing price problem, an example of such a constraint is that item1 is priced at least as much as item2. Our algorithm, called Lattice-Linear Predicate Detection (LLP) can be implemented in parallel without any locks or compare-and-set instructions. It just assumes atomicity of reads and writes.