Despite rapid adoption and deployment of large language models (LLMs), the internal
computations of these models remain opaque and poorly understood. In this work, we seek …

Discovering governing equations of complex dynamical systems directly from data is a
central problem in scientific machine learning. In recent years, the sparse identification of …

The sheer size of modern neural networks makes model serving a serious computational
challenge. A popular class of compression techniques overcomes this challenge by pruning …

Grouped variable selection with discrete optimization: Computational and statistical
perspectives Page 1 The Annals of Statistics 2023, Vol. 51, No. 1, 1–32 https://doi.org/10.1214/21-AOS2155 …

In this paper, we first introduce a preconditioned primal-dual gradient algorithm based on
conjugate duality theory. This algorithm is designed to solve composite optimization problem …

We propose a scheme for solving mixed-integer programming problems in which the
optimization problem is translated to a ground-state preparation problem on a set of bosonic …

This paper presents the distributed primal outer approximation (DiPOA) algorithm for solving
sparse convex programming (SCP) problems with separable structures, efficiently, and in a …

The availability of passenger demand estimates for air traffic routes is crucial to a plethora of
application and research problems ranging from, eg, optimization of airline fleet utilization to …

We consider the sparse optimization problem with nonlinear constraints and an objective
function, which is given by the sum of a general smooth mapping and an additional term …

We consider the nonconvex set $\mathcal S_n=\{(x, X, z): X= xx^ T,\; x (1-z)= 0,\; x\geq 0,\;
z\in\{0, 1\}^ n\} $, which is closely related to the feasible region of several difficult nonconvex …