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New research details non-ergodic Markovian mirror descent with unbounded noise

By PulseAugur Editorial · [1 sources] ·

Researchers Yunus Alp Biyikoglu and arXiv have published a new paper detailing a method for analyzing stochastic mirror descent. Their work focuses on minimizing convex objectives within Markov chains, utilizing noisy subgradient observations from a single sample path. The paper establishes an O(T^{-1/2}) bound on the expected suboptimality gap for weighted-average iterates, even with unbounded stochastic subgradients and Poisson solutions. This analysis is applicable to both finite-state and irreducible countable-state Markov chains, provided certain regularity and moment conditions are met. AI

IMPACT Provides theoretical advancements in optimization techniques relevant to machine learning.

RANK_REASON Academic paper on optimization algorithms. [lever_c_demoted from research: ic=1 ai=0.7]

Read on arXiv cs.LG →

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

New research details non-ergodic Markovian mirror descent with unbounded noise

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Academic paper on optimization algorithms. [lever_c_demoted from research: ic=1 ai=0.7]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Yunus Alp Biyikoglu, R. Srikant ·

    Non-Ergodic Markovian Mirror Descent with Unbounded Noise

    arXiv:2610.34433v1 Announce Type: cross Abstract: We analyze stochastic mirror descent for minimizing a convex objective expressed as an expectation under the stationary distribution of a Markov chain, using noisy subgradient observations along a single sample path. Through Poiss…

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