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Showing 1 to 6 of 6 for “"Linear assignment problem (LAP)"”.
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GPU accelerated Hungarian algorithm for traveling salesman problem
… we present a model of the Traveling Salesman Problem (TSP) cast in a quadratic assignment problem framework with linearized objective function and constraints. This is referred to as Reformulation Linearization Technique at Level 2 (or RLT2). We apply dual ascent procedure for obtaining lower …
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Theoretical and computational advances in finite-size facility placement and assignment problems
… costs. Main difficulty of this optimization problem lies in its continuous non-convex/non-concave feasible space, which makes it tough to escape local optimality. Through this research, novel approaches will be proposed which can be used to distill this continuous space into a finite set of …
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Towards GPU-accelerated discrete optimization
… crucial role in solving complex decision-making problems across a wide range of applications in operations research. These problems often involve selecting an optimal subset or sequence of decisions from a finite set, a task that becomes exponentially harder as the problem size grows. While …
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GPU-based Lagrangian heuristic for multidimensional assignment problems with decomposable costs
Multidimensional assignment problem (MAP) is one of the many formulations of data association problem which categorizes data based on various data sources. A higher number of data sources ensures an accurate categorization of data. But it also leads to a significant increase in the amount of data, …
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Correspondence among connectomes as combinatorial optimization
… groups of subjects, which creates two important problems: aligning tractography data across subjects, a problem called tractogram alignment, and the extraction of tracts of interest, named tract segmentation problem. Due to the anatomical variability across subjects, these two problems are …
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GPU-accelerated algorithms for the resource-constrained assignment problem
The student, Olivia Reynen, accepted the attached license on 2020-05-01 at 19:39.