In general, finding a maximum cut is computationally hard.

Verifying this dominance is computationally hard, so it can only be used with a dynamic programming approach.

Computing the tree-depth is computationally hard: the corresponding decision problem is NP-complete.

Graph coloring is computationally hard.

Finding the global optimum is a computationally hard problem.

It is computationally hard to find the exact value of the discrepancy of large point sets.

In most multi-robot systems, an individual robot is not capable of solving computationally hard problems due to lack of high processing power.

Processing and cost, both, are hard constraints that emphasize simplicity of the individual robots, and thus motivate a cluster approach to solve computationally hard problems.

In computer science, local search is a metaheuristic method for solving computationally hard optimization problems.

However, finding a maximum-size domatic partition is computationally hard.