A Compressive Sensing Inspired Monte-Carlo Method for Combinatorial Optimization

In this paper, we present a Monte-Carlo Compressive Optimization algorithm, a new method to tackle combinatorial optimization problems, including Black-Box or complicated objective functions. The method relies on random queries to the objective function in order to estimate generalized moments. Next, a greedy algorithm from compressive sensing is repurposed to find the global optimum when not overfitting to the samples. We provide numerical results giving evidence that our method is competitive by comparing it with dual annealing. Moreover, we give theoretical justification for the success of the algorithm and analyze its properties. The practicality of our algorithm is enhanced by the ability to tune the heuristic parameters to the available computational resources. An end-to-end open-source implementation is available to use our method.