An accelerated Newton-dinkelbach method and its application to two variables per inequality systems

Dadush, Daniel, Koh, Zhuan Khye, Natura, Bento and Végh, László A. ORCID: 0000-0003-1152-200X (2021) An accelerated Newton-dinkelbach method and its application to two variables per inequality systems. In: Mutzel, Petra, Pagh, Rasmus and Herman, Grzegorz, (eds.) 29th Annual European Symposium on Algorithms, ESA 2021. Leibniz International Proceedings in Informatics, LIPIcs. Schloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing. ISBN 9783959772044

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Abstract

We present an accelerated, or “look-ahead” version of the Newton-Dinkelbach method, a well-known technique for solving fractional and parametric optimization problems. This acceleration halves the Bregman divergence between the current iterate and the optimal solution within every two iterations. Using the Bregman divergence as a potential in conjunction with combinatorial arguments, we obtain strongly polynomial algorithms in three applications domains: (i) For linear fractional combinatorial optimization, we show a convergence bound of O(m log m) iterations; the previous best bound was O(m2 log m) by Wang et al. (2006). (ii) We obtain a strongly polynomial label-correcting algorithm for solving linear feasibility systems with two variables per inequality (2VPI). For a 2VPI system with n variables and m constraints, our algorithm runs in O(mn) iterations. Every iteration takes O(mn) time for general 2VPI systems, and O(m + n log n) time for the special case of deterministic Markov Decision Processes (DMDPs). This extends and strengthens a previous result by Madani (2002) that showed a weakly polynomial bound for a variant of the Newton-Dinkelbach method for solving DMDPs. (iii) We give a simplified variant of the parametric submodular function minimization result by Goemans et al. (2017).

Item Type:	Book Section
Official URL:	https://drops.dagstuhl.de/opus/institut_lipics.php
Additional Information:	© 2021 The Authors
Divisions:	Mathematics
Subjects:	Q Science > QA Mathematics Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Date Deposited:	30 Sep 2021 13:06
Last Modified:	11 Dec 2024 18:05
URI:	http://eprints.lse.ac.uk/id/eprint/112158

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