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Tradeoffs between convergence rate and noise amplification for momentum-based accelerated optimization algorithms
Finally, by analyzing a class of accelerated gradient flow dynamics, whose suitable discretization yields the two-step momentum algorithm, we establish that stochastic performance tradeoffs also extend to continuous time.
Transient growth of accelerated optimization algorithms
For convex quadratic problems, we employ tools from linear systems theory to show that transient growth arises from the presence of non-normal dynamics.
Learning the model-free linear quadratic regulator via random search
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.
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 action for an unknown dynamical system by directly searching over the parameter space of controllers.
Robustness of accelerated first-order algorithms for strongly convex optimization problems
We study the robustness of accelerated first-order algorithms to stochastic uncertainties in gradient evaluation.
Proximal algorithms for large-scale statistical modeling and sensor/actuator selection
Several problems in modeling and control of stochastically-driven dynamical systems can be cast as regularized semi-definite programs.
