Zobrazeno 1 - 10
of 21
pro vyhledávání: '"Paulus, Max B."'
Cutting planes are crucial in solving mixed integer linear programs (MILP) as they facilitate bound improvements on the optimal solution. Modern MILP solvers rely on a variety of separators to generate a diverse set of cutting planes by invoking the
Externí odkaz:
http://arxiv.org/abs/2311.05650
Autor:
Paulus, Max B., Krause, Andreas
Primal heuristics are important for solving mixed integer linear programs, because they find feasible solutions that facilitate branch and bound search. A prominent group of primal heuristics are diving heuristics. They iteratively modify and resolve
Externí odkaz:
http://arxiv.org/abs/2301.09943
Autor:
Miladinović, Đorđe, Shridhar, Kumar, Jain, Kushal, Paulus, Max B., Buhmann, Joachim M., Sachan, Mrinmaya, Allen, Carl
In principle, applying variational autoencoders (VAEs) to sequential data offers a method for controlled sequence generation, manipulation, and structured representation learning. However, training sequence VAEs is challenging: autoregressive decoder
Externí odkaz:
http://arxiv.org/abs/2209.12590
Cutting planes are essential for solving mixed-integer linear problems (MILPs), because they facilitate bound improvements on the optimal solution value. For selecting cuts, modern solvers rely on manually designed heuristics that are tuned to gauge
Externí odkaz:
http://arxiv.org/abs/2206.13414
Supervised learning can improve the design of state-of-the-art solvers for combinatorial problems, but labelling large numbers of combinatorial instances is often impractical due to exponential worst-case complexity. Inspired by the recent success of
Externí odkaz:
http://arxiv.org/abs/2202.08396
Autor:
Valentin, Romeo, Ferrari, Claudio, Scheurer, Jérémy, Amrollahi, Andisheh, Wendler, Chris, Paulus, Max B.
We present our submission for the configuration task of the Machine Learning for Combinatorial Optimization (ML4CO) NeurIPS 2021 competition. The configuration task is to predict a good configuration of the open-source solver SCIP to solve a mixed in
Externí odkaz:
http://arxiv.org/abs/2202.04910
The Gumbel-max trick is a method to draw a sample from a categorical distribution, given by its unnormalized (log-)probabilities. Over the past years, the machine learning community has proposed several extensions of this trick to facilitate, e.g., d
Externí odkaz:
http://arxiv.org/abs/2110.01515
Gradient estimation in models with discrete latent variables is a challenging problem, because the simplest unbiased estimators tend to have high variance. To counteract this, modern estimators either introduce bias, rely on multiple function evaluat
Externí odkaz:
http://arxiv.org/abs/2010.04838
The Gumbel-Max trick is the basis of many relaxed gradient estimators. These estimators are easy to implement and low variance, but the goal of scaling them comprehensively to large combinatorial distributions is still outstanding. Working within the
Externí odkaz:
http://arxiv.org/abs/2006.08063
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.