In this paper, we present a new policy gradient (PG) method, namely, the block policy mirror descent (BPMD) method, for solving a class of regularized reinforcement learning (RL) problems with (str…
One fundamental problem in constrained decentralized multiagent optimization is the trade-off between gradient/sampling complexity and communication complexity. In this paper, we propose new algori…
This paper studies the communication complexity of convex risk-averse optimization over a network. The problem generalizes the well-studied risk-neutral finite-sum distributed optimization problem,…
In this paper we first present a novel operator extrapolation (OE) method for solving deterministic variational inequality (VI) problems. Similar to the gradient (operator) projection method, OE up…
The focus of this paper is on stochastic variational inequalities (VI) under Markovian noise. A prominent application of our algorithmic developments is the stochastic policy evaluation problem in …
Researches on high coherence light sources always pursue fast tuning speed, linear tunability and persistence of narrow linewidths at different tuning channels. Fortunately, acousto-optical interac…
Conditional gradient methods have attracted much attention in both machine learning and optimization communities recently. These simple methods can guarantee the generation of sparse solutions. In …
Recently, there has been a growing interest in distributionally robust optimization (DRO) as a principled approach to data-driven decision making. In this paper, we consider a distributionally robu…