307 citations · 693 across the 70 of their papers we have counts for
12 papers · 1 filter
A GPU-accelerated Nonlinear Branch-and-Bound Framework for Sparse Linear Models
Ryan Lucas, Xiang Meng, Rahul Mazumder
We study exact sparse linear regression with an penalty and develop a branch-and-bound (BnB) algorithm explicitly designed for GPU execution. Starting from a perspe…
Linear programming using diagonal linear networks
Haoyue Wang, Promit Ghosal, Rahul Mazumder
Linear programming has played a crucial role in shaping decision-making, resource allocation, and cost reduction in various domains. In this paper, we investigate the application o…
A Cyclic Coordinate Descent Method for Convex Optimization on Polytopes
Rahul Mazumder, Haoyue Wang
Coordinate descent algorithms are popular for huge-scale optimization problems due to their low cost per-iteration. Coordinate descent methods apply to problems where the constrain…
A Light-speed Linear Program Solver for Personalized Recommendation with Diversity Constraints
Haoyue Wang, Miao Cheng, Kinjal Basu +3
We study a structured linear program (LP) that emerges in the need of ranking candidates or items in personalized recommender systems. Since the candidate set is only known in real…
Linear regression with partially mismatched data: local search with theoretical guarantees
Rahul Mazumder, Haoyue Wang
Linear regression is a fundamental modeling tool in statistics and related fields. In this paper, we study an important variant of linear regression in which the predictor-response…
Frank-Wolfe Methods with an Unbounded Feasible Region and Applications to Structured Learning
Haoyue Wang, Haihao Lu, Rahul Mazumder
The Frank-Wolfe (FW) method is a popular algorithm for solving large-scale convex optimization problems appearing in structured statistical learning. However, the traditional Frank…