An Accelerated Newton–Dinkelbach Method and Its Application to Two Variables per Inequality Systems

Daniel Dadush (), Zhuan Khye Koh (), Bento Natura () and László A. Végh ()
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Daniel Dadush: Centrum Wiskunde & Informatica, Amsterdam 1098 XG, Netherlands
Zhuan Khye Koh: Department of Mathematics, London School of Economics and Political Science, London WC2A 2AE, United Kingdom
Bento Natura: Department of Mathematics, London School of Economics and Political Science, London WC2A 2AE, United Kingdom
László A. Végh: Department of Mathematics, London School of Economics and Political Science, London WC2A 2AE, United Kingdom

Mathematics of Operations Research, 2023, vol. 48, issue 4, 1934-1958

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 ( m 2 log m ) by Wang, Yang, and Zhang from 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 from 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 from 2017 by Goemans, Gupta, and Jaillet.

Keywords: Primary: 49M15; 90C32; 90C05; secondary: 90C27; 90C40; 68W40; Newton–Dinkelbach method; fractional optimization; parametric optimization; strongly polynomial algorithm; two variables per inequality system; Markov decision process; submodular function minimization (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:inm:ormoor:v:48:y:2023:i:4:p:1934-1958

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