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Found 21 papers, 0 papers with code
Stability properties of gradient flow dynamics for the symmetric low-rank matrix factorization problem
We demonstrate that the Schur complement to a principal eigenspace of the target matrix is governed by an autonomous system that is decoupled from the rest of the dynamics.
Tannenbaum's gain-margin optimization meets Polyak's heavy-ball algorithm
The link between first-order optimization methods and robust control theory sheds new light into limits of algorithmic performance for such methods, and suggests a new framework where similar computational problems can be systematically studied and algorithms optimized.
From exponential to finite/fixed-time stability: Applications to optimization
The development of finite/fixed-time stable optimization algorithms typically involves study of specific problem instances.
Stability of Primal-Dual Gradient Flow Dynamics for Multi-Block Convex Optimization Problems
We examine stability properties of primal-dual gradient flow dynamics for composite convex optimization problems with multiple, possibly nonsmooth, terms in the objective function under the generalized consensus constraint.
Accelerated forward-backward and Douglas-Rachford splitting dynamics
We examine convergence properties of continuous-time variants of accelerated Forward-Backward (FB) and Douglas-Rachford (DR) splitting algorithms for nonsmooth composite optimization problems.
Provably Efficient Generalized Lagrangian Policy Optimization for Safe Multi-Agent Reinforcement Learning
We examine online safe multi-agent reinforcement learning using constrained Markov games in which agents compete by maximizing their expected total rewards under a constraint on expected total utilities.
Multi-agent Reinforcement Learning
reinforcement-learning
+2
Tradeoffs between convergence rate and noise amplification for momentum-based accelerated optimization algorithms
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.
Convergence and sample complexity of natural policy gradient primal-dual methods for constrained MDPs
We study sequential decision making problems aimed at maximizing the expected total reward while satisfying a constraint on the expected total utility.
Independent Policy Gradient for Large-Scale Markov Potential Games: Sharper Rates, Function Approximation, and Game-Agnostic Convergence
When there is no uncertainty in the gradient evaluation, we show that our algorithm finds an $\epsilon$-Nash equilibrium with $O(1/\epsilon^2)$ iteration complexity which does not explicitly depend on the state space size.
Multi-agent Reinforcement Learning
Policy Gradient Methods
+1
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.
Optimal Network Topology of Multi-Agent Systems subject to Computation and Communication Latency (with proofs)
We study minimum-variance feedback-control design for a networked control system with retarded dynamics, where inter-agent communication is subject to latency.
Well-conditioned ultraspherical and spectral integration methods for resolvent analysis of channel flows of Newtonian and viscoelastic fluids
Modal and nonmodal analyses of fluid flows provide fundamental insight into the early stages of transition to turbulence.
Fluid Dynamics Numerical Analysis Analysis of PDEs Dynamical Systems Numerical Analysis Optimization and Control
Provably Efficient Safe Exploration via Primal-Dual Policy Optimization
To this end, we present an \underline{O}ptimistic \underline{P}rimal-\underline{D}ual Proximal Policy \underline{OP}timization (OPDOP) algorithm where the value function is estimated by combining the least-squares policy evaluation and an additional bonus term for safe exploration.
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.
Global exponential stability of primal-dual gradient flow dynamics based on the proximal augmented Lagrangian: A Lyapunov-based approach
For a class of nonsmooth composite optimization problems with linear equality constraints, we utilize a Lyapunov-based approach to establish the global exponential stability of the primal-dual gradient flow dynamics based on the proximal augmented Lagrangian.
Stochastic dynamical modeling of turbulent flows
Drawing on this abundance of data, dynamical models can be constructed to reproduce structural and statistical features of turbulent flows, opening the way to the design of effective model-based flow control strategies.
Proximal gradient flow and Douglas-Rachford splitting dynamics: global exponential stability via integral quadratic constraints
In our analysis, we use the fact that these algorithms can be interpreted as variable-metric gradient methods on the suitable envelopes and exploit structural properties of the nonlinear terms that arise from the gradient of the smooth part of the objective function and the proximal operator associated with the nonsmooth regularizer.
Fast Multi-Agent Temporal-Difference Learning via Homotopy Stochastic Primal-Dual Optimization
We study the policy evaluation problem in multi-agent reinforcement learning where a group of agents, with jointly observed states and private local actions and rewards, collaborate to learn the value function of a given policy via local computation and communication over a connected undirected network.
Multi-agent Reinforcement Learning
Reinforcement Learning
+1
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.
A second order primal-dual method for nonsmooth convex composite optimization
We develop a second order primal-dual method for optimization problems in which the objective function is given by the sum of a strongly convex twice differentiable term and a possibly nondifferentiable convex regularizer.
