Zobrazeno 1 - 5
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pro vyhledávání: '"Paul, Anik Kumar"'
This letter investigates the convergence and concentration properties of the Stochastic Mirror Descent (SMD) algorithm utilizing biased stochastic subgradients. We establish the almost sure convergence of the algorithm's iterates under the assumption
Externí odkaz:
http://arxiv.org/abs/2407.05863
Saddle point problems, ubiquitous in optimization, extend beyond game theory to diverse domains like power networks and reinforcement learning. This paper presents novel approaches to tackle saddle point problem, with a focus on continuous-time conte
Externí odkaz:
http://arxiv.org/abs/2404.04907
This letter presents an almost sure convergence of the zeroth-order mirror descent algorithm. The algorithm admits non-smooth convex functions and a biased oracle which only provides noisy function value at any desired point. We approximate the subgr
Externí odkaz:
http://arxiv.org/abs/2303.09793
Publikováno v:
In IFAC PapersOnLine 2022 55(30):448-453
