Learning Reduced Order Dynamics via Geometric Representations
Imran Nasim, Melanie Weber
SCML 2024
We deal with the linear programming relaxation of set partitioning problems arising in airline crew scheduling. Some of these linear programs have been extremely difficult to solve with the traditional algorithms. We have used an extension of the subgradient algorithm, the volume algorithm, to produce primal solutions that might violate the constraints by at most 2%, and that are within 1% of the lower bound. This method is fast, requires minimal storage, and can be parallelized in a straightforward way. © 2002 Elsevier Science B.V.
Imran Nasim, Melanie Weber
SCML 2024
Arnon Amir, Michael Lindenbaum
IEEE Transactions on Pattern Analysis and Machine Intelligence
A.R. Conn, Nick Gould, et al.
Mathematics of Computation
Charles A Micchelli
Journal of Approximation Theory