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Min-Max Optimization Needs Exponential Queries, Study Finds

By PulseAugur Editorial · [1 sources] ·

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

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

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

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Min-Max Optimization Needs Exponential Queries, Study Finds

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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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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Alexandros Hollender ·

    Min-Max Optimization Requires Exponentially Many Queries

    We study the query complexity of min-max optimization of a nonconvex-nonconcave function $f$ over $[0,1]^d \times [0,1]^d$. We show that, given oracle access to $f$ and to its gradient $\nabla f$, any algorithm that finds an $\varepsilon$-approximate stationary point must make a …

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