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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

TaskDatasetResultRank
Decision Making under Imperfect RecallDetection Game Benchmarks 1.0 (full set)
Value Score18
11
Decision Making under Imperfect RecallRandom (Rand) Game Benchmarks 1.0 (full set)
Value0.55
8
Decision Making under Imperfect Recall61 aggregate benchmark instances (Sim, Det, and Rand)
Utility (% of Best)44.3
8
Simulation ProblemSim 540k
Value Score8.54
7
Subgroup DetectionDet 1k
Detection Value13
7
Random ProblemRand-24k
Value66
7
Decision Making under Imperfect RecallSimulation (Sim) Game Benchmarks 1.0 (full set)
Value10.38
6
Subgroup DetectionDet-2.2m
Performance Score16.2
6
Subgroup DetectionDet 10m
Performance Value24.64
6
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