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A continuous-time MaxSAT solver with high analog performance.

ABSTRACT: Many real-life optimization problems can be formulated in Boolean logic as MaxSAT, a class of problems where the task is finding Boolean assignments to variables satisfying the maximum number of logical constraints. Since MaxSAT is NP-hard, no algorithm is known to efficiently solve these problems. Here we present a continuous-time analog solver for MaxSAT and show that the scaling of the escape rate, an invariant of the solver's dynamics, can predict the maximum number of satisfiable constraints, often well before finding the optimal assignment. Simulating the solver, we illustrate its performance on MaxSAT competition problems, then apply it to two-color Ramsey number R(m, m) problems. Although it finds colorings without monochromatic 5-cliques of complete graphs on N???42 vertices, the best coloring for N?=?43 has two monochromatic 5-cliques, supporting the conjecture that R(5, 5)?=?43. This approach shows the potential of continuous-time analog dynamical systems as algorithms for discrete optimization.

SUBMITTER: Molnar B

PROVIDER: S-EPMC6242876 | biostudies-other | 2018 Nov

REPOSITORIES: biostudies-other

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A continuous-time MaxSAT solver with high analog performance.

Molnár Botond B Molnár Ferenc F Varga Melinda M Toroczkai Zoltán Z Ercsey-Ravasz Mária M

Nature communications 20181119 1

Many real-life optimization problems can be formulated in Boolean logic as MaxSAT, a class of problems where the task is finding Boolean assignments to variables satisfying the maximum number of logical constraints. Since MaxSAT is NP-hard, no algorithm is known to efficiently solve these problems. Here we present a continuous-time analog solver for MaxSAT and show that the scaling of the escape rate, an invariant of the solver's dynamics, can predict the maximum number of satisfiable constraint ...[more]

PMID: 30451849

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Project description:Wildland fires can emit substantial amounts of air pollution that may pose a risk to those in proximity (e.g., first responders, nearby residents) as well as downwind populations. Quickly deploying air pollution measurement capabilities in response to incidents has been limited to date by the cost, complexity of implementation, and measurement accuracy. Emerging technologies including miniaturized direct-reading sensors, compact microprocessors, and wireless data communications provide new opportunities to detect air pollution in real time. The U.S. Environmental Protection Agency (EPA) partnered with other U.S. federal agencies (CDC, NASA, NPS, NOAA, USFS) to sponsor the Wildland Fire Sensor Challenge. EPA and partnering organizations share the desire to advance wildland fire air measurement technology to be easier to deploy, suitable to use for high concentration events, and durable to withstand difficult field conditions, with the ability to report high time resolution data continuously and wirelessly. The Wildland Fire Sensor Challenge encouraged innovation worldwide to develop sensor prototypes capable of measuring fine particulate matter (PM2.5), carbon monoxide (CO), carbon dioxide (CO2), and ozone (O3) during wildfire episodes. The importance of using federal reference method (FRM) versus federal equivalent method (FEM) instruments to evaluate performance in biomass smoke is discussed. Ten solvers from three countries submitted sensor systems for evaluation as part of the challenge. The sensor evaluation results including sensor accuracy, precision, linearity, and operability are presented and discussed, and three challenge winners are announced. Raw solver submitted PM2.5 sensor accuracies of the winners ranged from ~22 to 32%, while smoke specific EPA regression calibrations improved the accuracies to ~75-83% demonstrating the potential of these systems in providing reasonable accuracies over conditions that are typical during wildland fire events.

Dataset Information

A continuous-time MaxSAT solver with high analog performance.

Publications

A continuous-time MaxSAT solver with high analog performance.

Similar Datasets

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