41 citations · 51 across the 8 of their papers we have counts for
8 papers
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…
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…
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…
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…
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…
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…
Estimating the Region of Attraction Using Polynomial Optimization: a Converse Lyapunov Result
Hesameddin Mohammadi, Matthew M. Peet
In this paper, we propose an iterative method for using SOS programming to estimate the region of attraction of a polynomial vector field, the conjectured convergence of which nece…
Combining SOS and Moment Relaxations with Branch and Bound to Extract Solutions to Global Polynomial Optimization Problems
Hesameddin Mohammadi, Matthew M. Peet
In this paper, we present a branch and bound algorithm for extracting approximate solutions to Global Polynomial Optimization (GPO) problems with bounded feasible sets. The algorit…