Optimization underpins many of the challenges that science and technology face on a daily
basis. Recent years have witnessed a major shift from traditional optimization paradigms …

This paper develops online algorithms to track solutions of time-varying constrained
optimization problems. Particularly, resembling workhorse Kalman filtering-based …

In this article, a distributed time‐varying convex optimization problem for multi‐agent
systems is studied. The goal of this article is to use only the local information and interaction …

Devising efficient algorithms that track the optimizers of continuously varying convex
optimization problems is key in many applications. A possible strategy is to sample the time …

We present a distributed optimization algorithm for solving online personalized optimization
problems over a network of computing and communicating nodes, each of which linked to a …

Spectrum sharing has been proposed as a promising way to increase the efficiency of
spectrum usage by allowing incumbent operators (IOs) to share their allocated radio …

In tracking of time-varying low-rank models of time-varying matrices, we present a method
robust to both uniformly-distributed measurement noise and arbitrarily-distributed “sparse” …

In this paper, we consider a recursive estimation problem for linear regression where the
signal to be estimated admits a sparse representation and measurement samples are only …

为了充分并有效地利用大量风电场之间的时空相关性, 在提高风电功率预测精度的同时保障计算
效率, 提出一种基于时空相关性的风电功率超短期自适应预测方法. 以向量自回归模型为基础 …

We consider non-differentiable convex optimization problems that vary continuously in time
and we propose algorithms that sample these problems at specific time instances and …