Min-Max Optimization Needs Exponential Queries, Study Finds

A new research paper explores the computational complexity of min-max optimization for non-convex and non-concave functions. The study demonstrates that finding an approximate stationary point for such functions requires an exponential number of queries, particularly concerning the approximation error and the dimensionality of the problem. AI

影响 This theoretical finding may impact the efficiency of training complex AI models that rely on min-max optimization techniques.

排序理由 The cluster contains a research paper detailing theoretical findings on optimization algorithms. [lever_c_demoted from research: ic=1 ai=1.0]

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