Finding neurons in a haystack: Case studies with sparse probing
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 …
computations of these models remain opaque and poorly understood. In this work, we seek …
Learning sparse nonlinear dynamics via mixed-integer optimization
D Bertsimas, W Gurnee - Nonlinear Dynamics, 2023 - Springer
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 …
central problem in scientific machine learning. In recent years, the sparse identification of …
Fast as chita: Neural network pruning with combinatorial optimization
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 …
challenge. A popular class of compression techniques overcomes this challenge by pruning …
Grouped variable selection with discrete optimization: Computational and statistical perspectives
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 …
perspectives Page 1 The Annals of Statistics 2023, Vol. 51, No. 1, 1–32 https://doi.org/10.1214/21-AOS2155 …
Preconditioned primal-dual gradient methods for nonconvex composite and finite-sum optimization
J Guo, X Wang, X Xiao - arXiv preprint arXiv:2309.13416, 2023 - arxiv.org
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 …
conjugate duality theory. This algorithm is designed to solve composite optimization problem …
Mixed-integer programming using a bosonic quantum computer
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 …
optimization problem is translated to a ground-state preparation problem on a set of bosonic …
Distributed primal outer approximation algorithm for sparse convex programming with separable structures
This paper presents the distributed primal outer approximation (DiPOA) algorithm for solving
sparse convex programming (SCP) problems with separable structures, efficiently, and in a …
sparse convex programming (SCP) problems with separable structures, efficiently, and in a …
Reproducible air passenger demand estimation
AM Tillmann, I Joormann, SCL Ammann - Journal of Air Transport …, 2023 - Elsevier
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 …
application and research problems ranging from, eg, optimization of airline fleet utilization to …
The sparse (st) optimization problem: Reformulations, optimality, stationarity, and numerical results
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 …
function, which is given by the sum of a general smooth mapping and an additional term …
Explicit convex hull description of bivariate quadratic sets with indicator variables
A De Rosa, A Khajavirad - arXiv preprint arXiv:2208.08703, 2022 - arxiv.org
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 …
z\in\{0, 1\}^ n\} $, which is closely related to the feasible region of several difficult nonconvex …