Abstract
In this work, the Lagrangian Search Method (LSM), an algorithmic framework based on lagrangian relaxation, relaxed decision procedures and binary search, is presented. LSM is used to efficiently extend the class of positive linear programs (PLP), a submodel of linear programming that admits very efficient approximations in sequential, parallel and distributed settings. Theoretical guarantees for the efficiency and the approximation ratio of LSM for extended PLP, are derived. The extended PLP model is used for fast parallel (and sequential) approximations to a class of hard combinatorial optimization problems.
Financial support from the Bodosaki Foundation to perform doctoral studies is gratefully announced. Bodosaki Foundation, Leoforos Amalias 20, 10557 Athina, Greece
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© 2001 Kluwer Academic Publishers
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Efraimidis, P.S., Spirakis, P.G. (2001). The Lagrangian Search Method. In: Hadjisavvas, N., Pardalos, P.M. (eds) Advances in Convex Analysis and Global Optimization. Nonconvex Optimization and Its Applications, vol 54. Springer, Boston, MA. https://doi.org/10.1007/978-1-4613-0279-7_15
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DOI: https://doi.org/10.1007/978-1-4613-0279-7_15
Publisher Name: Springer, Boston, MA
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