This article presents a powerful algorithmic framework for big data optimization, called the
block successive upper-bound minimization (BSUM). The BSUM includes as special cases …

The concept of Multi-access Edge Computing (MEC) has been recently introduced to
supplement cloud computing by deploying MEC servers to the network edge so as to reduce …

This book, as the title suggests, is about first-order methods, namely, methods that exploit
information on values and gradients/subgradients (but not Hessians) of the functions …

Nonconvex optimization arises in many areas of computational science and engineering.
However, most nonconvex optimization algorithms are only known to have local …

Without steering wheel and driver's seat, the self-driving cars will have new interior outlook
and spaces that can be used for enhanced infotainment services. For traveling people, self …

Backscatter communication which enables wireless-powered backscatter devices (BDs) to
transmit information by reflecting incident signals, is an energy-and cost-efficient …

Deep neural network compression techniques such as pruning and weight tensor
decomposition usually require fine-tuning to recover the prediction accuracy when the …

We consider the least squares regression problem, penalized with a combination of the ℓ _ 0
ℓ 0 and squared ℓ _ 2 ℓ 2 penalty functions (aka ℓ _0 ℓ _2 ℓ 0 ℓ 2 regularization). Recent …

In this paper we analyze the randomized block-coordinate descent (RBCD) methods
proposed in Nesterov (SIAM J Optim 22 (2): 341–362, 2012), Richtárik and Takáč (Math …

Stochastic gradient descent (SGD), also known as stochastic approximation, refers to certain
simple iterative structures used for solving stochastic optimization and root-finding problems …