Hesameddin Mohammadi | DeepAI

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Mahdi Soltanolkotabi
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Samantha Samuelson
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research

∙ 09/24/2022

Tradeoffs between convergence rate and noise amplification for momentum-based accelerated optimization algorithms

We study momentum-based first-order optimization algorithms in which the...

0 Hesameddin Mohammadi, et al. ∙

research

∙ 03/14/2021

Transient growth of accelerated first-order methods for strongly convex optimization problems

Optimization algorithms are increasingly being used in applications with...

0 Hesameddin Mohammadi, et al. ∙

research

∙ 12/26/2019

Convergence and sample complexity of gradient methods for the model-free linear quadratic regulator problem

Model-free reinforcement learning attempts to find an optimal control ac...

0 Hesameddin Mohammadi, et al. ∙

research

∙ 05/27/2019

Robustness of accelerated first-order algorithms for strongly convex optimization problems

We study the robustness of accelerated first-order algorithms to stochas...

0 Hesameddin Mohammadi, et al. ∙