activity
20162024
most citedTradeoffs between convergence rate and noise amplification for momentum-based accelerated optimization algorithms

3 citations · 3 across the 2 of their papers we have counts for

collaborators

8 papers

cs.LG2024

Stability properties of gradient flow dynamics for the symmetric low-rank matrix factorization problem

Hesameddin Mohammadi, Mohammad Tinati, Stephen Tu +2

The symmetric low-rank matrix factorization serves as a building block in many learning tasks, including matrix recovery and training of neural networks. However, despite a flurry…

math.OC2022★ 3 cited

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

Hesameddin Mohammadi, Meisam Razaviyayn, Mihailo R. Jovanović

We study momentum-based first-order optimization algorithms in which the iterations utilize information from the two previous steps and are subject to an additive white noise. This…

math.OC2021

Transient growth of accelerated optimization algorithms

Hesameddin Mohammadi, Samantha Samuelson, Mihailo R. Jovanović

Optimization algorithms are increasingly being used in applications with limited time budgets. In many real-time and embedded scenarios, only a few iterations can be performed and…

math.OC2019

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

Hesameddin Mohammadi, Armin Zare, Mahdi Soltanolkotabi +1

Model-free reinforcement learning attempts to find an optimal control action for an unknown dynamical system by directly searching over the parameter space of controllers. The conv…

math.OC2019

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

Hesameddin Mohammadi, Meisam Razaviyayn, Mihailo R. Jovanović

We study the robustness of accelerated first-order algorithms to stochastic uncertainties in gradient evaluation. Specifically, for unconstrained, smooth, strongly convex optimizat…

math.OC2018

Proximal algorithms for large-scale statistical modeling and sensor/actuator selection

Armin Zare, Hesameddin Mohammadi, Neil K. Dhingra +2

Several problems in modeling and control of stochastically-driven dynamical systems can be cast as regularized semi-definite programs. We examine two such representative problems a…