A survey of recent advances in optimization methods for wireless communications
Mathematical optimization is now widely regarded as an indispensable modeling and
solution tool for the design of wireless communications systems. While optimization has …
solution tool for the design of wireless communications systems. While optimization has …
A survey on some recent developments of alternating direction method of multipliers
DR Han - Journal of the Operations Research Society of China, 2022 - Springer
Recently, alternating direction method of multipliers (ADMM) attracts much attentions from
various fields and there are many variant versions tailored for different models. Moreover, its …
various fields and there are many variant versions tailored for different models. Moreover, its …
On the theory of policy gradient methods: Optimality, approximation, and distribution shift
Policy gradient methods are among the most effective methods in challenging reinforcement
learning problems with large state and/or action spaces. However, little is known about even …
learning problems with large state and/or action spaces. However, little is known about even …
Optimality and approximation with policy gradient methods in markov decision processes
Policy gradient (PG) methods are among the most effective methods in challenging
reinforcement learning problems with large state and/or action spaces. However, little is …
reinforcement learning problems with large state and/or action spaces. However, little is …
Policy gradient method for robust reinforcement learning
This paper develops the first policy gradient method with global optimality guarantee and
complexity analysis for robust reinforcement learning under model mismatch. Robust …
complexity analysis for robust reinforcement learning under model mismatch. Robust …
Denoising prior driven deep neural network for image restoration
Deep neural networks (DNNs) have shown very promising results for various image
restoration (IR) tasks. However, the design of network architectures remains a major …
restoration (IR) tasks. However, the design of network architectures remains a major …
The difficulty of computing stable and accurate neural networks: On the barriers of deep learning and Smale's 18th problem
Deep learning (DL) has had unprecedented success and is now entering scientific
computing with full force. However, current DL methods typically suffer from instability, even …
computing with full force. However, current DL methods typically suffer from instability, even …
Global convergence of ADMM in nonconvex nonsmooth optimization
In this paper, we analyze the convergence of the alternating direction method of multipliers
(ADMM) for minimizing a nonconvex and possibly nonsmooth objective function, ϕ (x_0 …
(ADMM) for minimizing a nonconvex and possibly nonsmooth objective function, ϕ (x_0 …
Acceleration methods
This monograph covers some recent advances in a range of acceleration techniques
frequently used in convex optimization. We first use quadratic optimization problems to …
frequently used in convex optimization. We first use quadratic optimization problems to …
Modern regularization methods for inverse problems
Regularization methods are a key tool in the solution of inverse problems. They are used to
introduce prior knowledge and allow a robust approximation of ill-posed (pseudo-) inverses …
introduce prior knowledge and allow a robust approximation of ill-posed (pseudo-) inverses …