
Policy Mirror Descent for Reinforcement Learning: Linear Convergence, New Sampling Complexity, and Generalized Problem Classes
We present new policy mirror descent (PMD) methods for solving reinforce...
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Simple and optimal methods for stochastic variational inequalities, II: Markovian noise and policy evaluation in reinforcement learning
The focus of this paper is on stochastic variational inequalities (VI) u...
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A Primal Approach to Constrained Policy Optimization: Global Optimality and FiniteTime Analysis
Safe reinforcement learning (SRL) problems are typically modeled as cons...
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Simple and optimal methods for stochastic variational inequalities, I: operator extrapolation
In this paper we first present a novel operator extrapolation (OE) metho...
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A Feasible Level Proximal Point Method for Nonconvex Sparse Constrained Optimization
Nonconvex sparse models have received significant attention in highdime...
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Conditional Gradient Methods for convex optimization with function constraints
Conditional gradient methods have attracted much attention in both machi...
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A Unified Singleloop Alternating Gradient Projection Algorithm for NonconvexConcave and ConvexNonconcave Minimax Problems
Much recent research effort has been directed to the development of effi...
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Complexity of Stochastic Dual Dynamic Programming
Stochastic dual dynamic programming is a cutting plane type algorithm fo...
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Proximal Point Methods for Optimization with Nonconvex Functional Constraints
Nonconvex optimization is becoming more and more important in machine le...
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GLAD: Learning Sparse Graph Recovery
Recovering sparse conditional independence graphs from data is a fundame...
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A unified variancereduced accelerated gradient method for convex optimization
We propose a novel randomized incremental gradient algorithm, namely, VA...
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Cubic Regularization with Momentum for Nonconvex Optimization
Momentum is a popular technique to accelerate the convergence in practic...
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Optimal Adaptive and Accelerated Stochastic Gradient Descent
Stochastic gradient descent (Sgd) methods are the most powerful optimiza...
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Complexity of Training ReLU Neural Network
In this paper, we explore some basic questions on the complexity of trai...
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Asynchronous decentralized accelerated stochastic gradient descent
In this work, we introduce an asynchronous decentralized accelerated sto...
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A Note on Inexact Condition for Cubic Regularized Newton's Method
This note considers the inexact cubicregularized Newton's method (CR), ...
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Sample Complexity of Stochastic VarianceReduced Cubic Regularization for Nonconvex Optimization
The popular cubic regularization (CR) method converges with first and s...
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Random gradient extrapolation for distributed and stochastic optimization
In this paper, we consider a class of finitesum convex optimization pro...
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Dynamic Stochastic Approximation for Multistage Stochastic Optimization
In this paper, we consider multistage stochastic optimization problems ...
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Conditional Accelerated Lazy Stochastic Gradient Descent
In this work we introduce a conditional accelerated lazy stochastic grad...
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Algorithms for stochastic optimization with expectation constraints
This paper considers the problem of minimizing an expectation function o...
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Generalized Uniformly Optimal Methods for Nonlinear Programming
In this paper, we present a generic framework to extend existing uniform...
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An optimal randomized incremental gradient method
In this paper, we consider a class of finitesum convex optimization pro...
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Stochastic First and Zerothorder Methods for Nonconvex Stochastic Programming
In this paper, we introduce a new stochastic approximation (SA) type alg...
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Guanghui Lan
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