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

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.

Funding: This project received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme [Grants 757481-ScaleOpt and 805241-QIP].