A Universally Optimal Multistage Accelerated Stochastic Gradient Method

面向多阶段随机储能套利的GPU加速动态规划

GPU-Accelerated Dynamic Programming for Multistage Stochastic Energy Storage Arbitrage

针对多阶段随机电价下储能套利的计算瓶颈，提出基于张量代数的GPU加速动态规划方法，适配非凹收益并生成合规报价曲线，实现大幅提速且保持近最优性。

A general model-and-run solver for multistage robust discrete linear optimization

The necessity to deal with uncertain data is a major challenge in decision making. Robust optimization emerged as one of the predominant paradigms to produce solutions that hedge against uncertainty. In order to obtain an even more realistic description of the underlying problem where the decision maker can react to newly disclosed information, multistage models can be used. However, due to their computational difficulty, multistage problems beyond two stages have received less attention and are often only addressed using approximation rather than optimization schemes. Even less attention is p…

重尾噪声下凸优化的加速随机一阶方法

Accelerated stochastic first-order method for convex optimization under heavy-tailed noise

针对重尾噪声下的凸复合优化问题，本文提出一种无需梯度裁剪或归一化的加速随机近端次梯度方法，证明其在光滑、弱光滑及非光滑等情况下均具有普遍最优的一阶预言机复杂度，并通过数值实验验证了理论结果。

Robust Distributed Accelerated Stochastic Gradient Methods for Multi-Agent Networks

We study distributed stochastic gradient (D-SG) method and its accelerated variant (D-ASG) for solving decentralized strongly convex stochastic optimization problems where the objective function is distributed over several computational units, lying on a fixed but arbitrary connected communication graph, subject to local communication constraints where noisy estimates of the gradients are available. We develop a framework which allows to choose the stepsize and the momentum parameters of these algorithms in a way to optimize performance by systematically trading off the bias, variance, robustn…

一种通用最优的对偶方法用于最小化异构组合

A Universally Optimal Primal-Dual Method for Minimizing Heterogeneous Compositions

本文提出了一种通用最优的对偶方法，用于最小化异构组合问题，通过构造新的常数来结合不同结构的优势，从而实现最优分析。

Robust Hybrid Zero-Order Optimization Algorithms with Acceleration via Averaging in Time

We study novel robust zero-order algorithms with acceleration for the solution of real-time optimization problems. In particular, we propose a family of extremum seeking dynamics that can be universally modeled as singularly perturbed hybrid dynamical systems with restarting mechanisms. From this family of dynamics, we synthesize four fast algorithms for the solution of convex, strongly convex, constrained, and unconstrained optimization problems. In each case, we establish robust semi-global practical asymptotic or exponential stability results, and we show how to obtain well-posed discretize…