Rangachari Anand, Kishan Mehrotra, et al.
IEEE Transactions on Neural Networks
Our objective is to boost the state-of-the-art performance in MaxSAT solving. To this end, we employ the instance- specific algorithm configurator ISAC, and improve it with the latest in portfolio technology. Experimental results on SAT show that this combination marks a significant step forward in our ability to tune algorithms instance-specifically. We then apply the new methodology to a number of MaxSAT problem domains and show that the resulting solvers consistently outperform the best existing solvers on the respective problem families. In fact, the solvers presented here were inde-pendently evaluated at the 2013 MaxSAT Evaluation where they won six of the eleven categories.
Rangachari Anand, Kishan Mehrotra, et al.
IEEE Transactions on Neural Networks
Dzung Phan, Vinicius Lima
INFORMS 2023
Jehanzeb Mirza, Leonid Karlinsky, et al.
NeurIPS 2023
Andre A. Cire, Serdar Kadioglu, et al.
AAAI 2014