Large Neighborhood Search (LNS) is a combinatorial optimization heuristic that starts with an assignment of values for the variables to be optimized, and iteratively improves it by searching a large neighborhood around the current assignment.
1 code implementation • 23 Dec 2020 • Vinod Nair, Sergey Bartunov, Felix Gimeno, Ingrid von Glehn, Pawel Lichocki, Ivan Lobov, Brendan O'Donoghue, Nicolas Sonnerat, Christian Tjandraatmadja, Pengming Wang, Ravichandra Addanki, Tharindi Hapuarachchi, Thomas Keck, James Keeling, Pushmeet Kohli, Ira Ktena, Yujia Li, Oriol Vinyals, Yori Zwols
Our approach constructs two corresponding neural network-based components, Neural Diving and Neural Branching, to use in a base MIP solver such as SCIP.
We share this observation in the hope that it helps the SAT community better understand the hardness of random instances used in competitions and inspire other interesting ideas on SAT solving.
We present a deep reinforcement learning approach to minimizing the execution cost of neural network computation graphs in an optimizing compiler.
We consider the problem of quantitatively evaluating missing value imputation algorithms.
In this paper we propose a structured output approach for missing value imputation that also incorporates domain constraints.
Our model achieves 6. 5% error on the test set, which is close to the best published result for NORB (5. 9%) using a convolutional neural net that has built-in knowledge of translation invariance.