Malik. On solving the partial MAX-SAT problem. In SAT, volume 4121 of LNCS, pages 252-265. Springer, 2006. URL: http://dx.doi.org/10.1007/ 11814948_25.Fu, Z., Malik, S.: On solving the partial MAX-SAT problem. In: Proceedings of the 9th International Conference on Theory and ...
Coalition Structure Generation (CSG) means partitioning agents into exhaustive and disjoint coalitions so that the sum of values of all the coalitions is maximized. Solving this problem could be facilitated by employing some compact representation schemes, such as marginal contribution network (MC-net)...
We address the computation of the winning alternative for some important rules, both by identifying the computational complexity of the relevant problems and by showing that for several of them, computing the winner reduces in a very natural way to a maxsat problem....
Reinforcement Learning for Solving the Vehicle Routing Problem Mohammadreza Nazari, Afshin Oroojlooy, Lawrence V. Snyder, Martin Takáč NeurIPS 2018 Attention, Learn to Solve Routing Problems! Wouter Kool, Herke van Hoof, Max Welling ICLR 2019 Learning a SAT Solver from Single-Bit Supervision ...
Before solving the optimization problem numerically, we note that the predictions for τ are relatively intuitive. In scenarios 1 and 2, τ should be set to the maximum stimulus value in the physical space, and the optimal resource allocation solutions remain the same as derived in our ...
Because of the restriction that some variables are integers, solving an ILP is a NP-Hard problem [64]. Another well known formalism is Boolean satisfiability (SAT). In this formalism, the variables are Boolean (binary), and so can be True (1) or False (0). A literal is either a ...
Then if y ∈ X satisfies ||y|| ≤ 1 10c , there exists a unique x ∈ X with ||x || ≤ 1 5c solving x + T x = y. The unique solution satisfies ||x|| ≤ 2 ||y||. Proof of Theorem 4.133. In the case that s is a section of rigid ASD instantons, we have that the ...
the singular values. In this sense, even a Wiener estimate, which applies a smooth mask on the singular values, is both more correct and should, if properly used, provide better results in general. Another important problem is that, despite their conceptual and computational complexity, the ...
The generalization solves the two-machine n-job open-shop problem through solving the two-machine flow-shop problem with n−1 jobs. The one “omitted” job is then added to the constructed flow-shop schedule, where it is first processed on the second machine before processing all the other...
For DBCG, the main solution time is spent on solving the pricing problems, while the primal heuristics used in the algorithm contribute to a substantial portion, ranging from 30 to 50% in each main iteration. It is important to note that the solution time presented in Table 2 for DBCG and...