1 citations · 1 across the 7 of their papers we have counts for
7 papers
A GPU-Accelerated Bi-linear ADMM Algorithm for Distributed Sparse Machine Learning
Alireza Olama, Andreas Lundell, Jan Kronqvist +2
This paper introduces the Bi-linear consensus Alternating Direction Method of Multipliers (Bi-cADMM), aimed at solving large-scale regularized Sparse Machine Learning (SML) problem…
A cutting plane algorithm for globally solving low dimensional k-means clustering problems
Martin Ryner, Jan Kronqvist, Johan Karlsson
Clustering is one of the most fundamental tools in data science and machine learning, and k-means clustering is one of the most common such methods. There is a variety of approxima…
Solution Polishing via Path Relinking for Continuous Black-Box Optimization
Dimitri Papageorgiou, Jan Kronqvist, Asha Ramanujam +3
When faced with a limited budget of function evaluations, state-of-the-art black-box optimization (BBO) solvers struggle to obtain globally, or sometimes even locally, optimal solu…
LineWalker: Line Search for Black Box Derivative-Free Optimization and Surrogate Model Construction
Dimitri J. Papageorgiou, Jan Kronqvist, Krishnan Kumaran
This paper describes a simple, but effective sampling method for optimizing and learning a discrete approximation (or surrogate) of a multi-dimensional function along a one-dimensi…
Globally solving the Gromov-Wasserstein problem for point clouds in low dimensional Euclidean spaces
Martin Ryner, Jan Kronqvist, Johan Karlsson
This paper presents a framework for computing the Gromov-Wasserstein problem between two sets of points in low dimensional spaces, where the discrepancy is the squared Euclidean no…
A Column Generation Approach for Radiation Therapy Patient Scheduling with Planned Machine Unavailability and Uncertain Future Arrivals
Sara Frimodig, Per Enqvist, Jan Kronqvist
The number of cancer cases per year is rapidly increasing worldwide. In radiation therapy (RT), radiation from linear accelerators is used to kill malignant tumor cells. Scheduling…