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An Accelerated Newton-Dinkelbach Method and its Application to Two Variables Per Inequality Systems
Data Structures and Algorithms · Submitted 18 Apr 2020
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim 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 et al. (2006).
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The abstract, sentence by sentence
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 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).
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | We present an accelerated, or 'look-ahead' version of the Newton-Dinkelbach method, a well-known technique for solving fractional and parametric optimization problems. | contribution 0.99 | 0.88 | 0.12 | asserted 0.99 |
| s1 | This acceleration halves the Bregman divergence between the current iterate and the optimal solution within every two iterations. | result 0.84 | 0.89 | 0.16 | asserted 1.00 |
| s2 | 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 et al. (2006). | result 0.66 | 0.92 | 0.33 | asserted 1.00 |
| s3 | (ii) We obtain a strongly polynomial label-correcting algorithm for solving linear feasibility systems with two variables per inequality (2VPI). | contribution 0.82 | 0.95 | 0.15 | asserted 1.00 |
| s4 | For a 2VPI system with $n$ variables and $m$ constraints, our algorithm runs in $O(mn)$ iterations. | result 0.97 | 0.95 | 0.12 | asserted 1.00 |
| s5 | 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). | result 0.95 | 0.92 | 0.08 | asserted 1.00 |
| s6 | 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. | interpretation 0.39 | 0.44 | 0.07 | asserted 1.00 |
| s7 | (iii) We give a simplified variant of the parametric submodular function minimization result by Goemans et al. (2017). | contribution 0.97 | 0.90 | 0.08 | asserted 1.00 |
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Original 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 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).
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