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AdaGrad optimization scheme shows convergence issues with composite objectives

By PulseAugur Editorial · [2 sources] ·

A new research paper published on arXiv details a limitation of the AdaGrad optimization algorithm when applied to composite objectives. The paper demonstrates a scenario where AdaGrad fails to achieve the expected convergence rate due to a mismatch between its accumulation mechanism and composite optimality. This occurs because the gradient of the smooth term may not diminish at the optimum, causing AdaGrad to excessively reduce its stepsize and slow down convergence. AI

IMPACT Highlights a theoretical limitation in optimization algorithms, potentially impacting the efficiency of training certain machine learning models.

RANK_REASON The cluster contains a research paper detailing a theoretical limitation of an optimization algorithm.

Read on arXiv stat.ML →

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AdaGrad optimization scheme shows convergence issues with composite objectives

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COVERAGE [2]

  1. arXiv stat.ML TIER_1 English(EN) · Matia Bojovic, Saverio Salzo, Massimiliano Pontil ·

    AdaGrad does not adapt to H\"older-smoothness for composite objectives

    arXiv:2606.29893v1 Announce Type: cross Abstract: We exhibit a simple deterministic one-dimensional convex composite optimization problem for which AdaGrad scheme does not achieve the classical convergence rate $\mathcal{O}(n^{-(1+\nu)/2})$ associated with H\"older-smooth objecti…

  2. arXiv stat.ML TIER_1 English(EN) · Massimiliano Pontil ·

    AdaGrad does not adapt to Hölder-smoothness for composite objectives

    We exhibit a simple deterministic one-dimensional convex composite optimization problem for which AdaGrad scheme does not achieve the classical convergence rate $\mathcal{O}(n^{-(1+ν)/2})$ associated with Hölder-smooth objectives. The example highlights a basic mismatch between c…

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