No abstract

Combinatorial optimization (CO) is a longstanding challenging task not only in its inherent complexity (e.g. NP-hard) but also the possible sensitivity to input conditions. In this paper, we take an initiative on developing the mechanisms for adversarial attack and defense towards combinatorial optimization solvers, whereby the solver is treated as a black-box function and the original problem's underlying graph structure (which is often available and associated with the problem instance, e.g. DAG, TSP) is attacked under a given budget. In particular, we present a simple yet effective defense strategy to modify the graph structure to increase the robustness of solvers, which shows its universal effectiveness across tasks and solvers.1 Readers may argue that there are little deliberate attacks to CO solvers, while one can regard such attacks as the problem instance variation which can often happen in real-world e.g. when the network takes a small daily change in Directed Acyclic Graph (DAG) as will be studied in our experiments.

No abstract