The Complexity of Gradient Descent: CLS = PPAD $\cap$ PLS
About
We study search problems that can be solved by performing Gradient Descent on a bounded convex polytopal domain and show that this class is equal to the intersection of two well-known classes: PPAD and PLS. As our main underlying technical contribution, we show that computing a Karush-Kuhn-Tucker (KKT) point of a continuously differentiable function over the domain $[0,1]^2$ is PPAD $\cap$ PLS-complete. This is the first non-artificial problem to be shown complete for this class. Our results also imply that the class CLS (Continuous Local Search) - which was defined by Daskalakis and Papadimitriou as a more "natural" counterpart to PPAD $\cap$ PLS and contains many interesting problems - is itself equal to PPAD $\cap$ PLS.
John Fearnley, Paul W. Goldberg, Alexandros Hollender, Rahul Savani• 2020
Related benchmarks
| Task | Dataset | Result | Rank | |
|---|---|---|---|---|
| Decision Making under Imperfect Recall | Detection Game Benchmarks 1.0 (full set) | Value Score18 | 11 | |
| Decision Making under Imperfect Recall | Random (Rand) Game Benchmarks 1.0 (full set) | Value0.55 | 8 | |
| Decision Making under Imperfect Recall | 61 aggregate benchmark instances (Sim, Det, and Rand) | Utility (% of Best)44.3 | 8 | |
| Simulation Problem | Sim 540k | Value Score8.54 | 7 | |
| Subgroup Detection | Det 1k | Detection Value13 | 7 | |
| Random Problem | Rand-24k | Value66 | 7 | |
| Decision Making under Imperfect Recall | Simulation (Sim) Game Benchmarks 1.0 (full set) | Value10.38 | 6 | |
| Subgroup Detection | Det-2.2m | Performance Score16.2 | 6 | |
| Subgroup Detection | Det 10m | Performance Value24.64 | 6 |
Showing 9 of 9 rows