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of 337
pro vyhledávání: '"Sahinidis, Nikolaos V."'
Recent advances in the efficiency and robustness of algorithms solving convex quadratically constrained quadratic programming (QCQP) problems motivate developing techniques for creating convex quadratic relaxations that, although more expensive to co
Externí odkaz:
http://arxiv.org/abs/2408.13058
When computing bounds, spatial branch-and-bound algorithms often linearly outer approximate convex relaxations for non-convex expressions in order to capitalize on the efficiency and robustness of linear programming solvers. Considering that linear o
Externí odkaz:
http://arxiv.org/abs/2408.13053
Gas networks are used to transport natural gas, which is an important resource for both residential and industrial customers throughout the world. The gas network design problem is generally modelled as a nonconvex mixed-integer nonlinear integer pro
Externí odkaz:
http://arxiv.org/abs/2307.07648
This paper investigates the effectiveness of transfer learning based on Mallows' Cp. We propose a procedure that combines transfer learning with Mallows' Cp (TLCp) and prove that it outperforms the conventional Mallows' Cp criterion in terms of accur
Externí odkaz:
http://arxiv.org/abs/2107.02847
We consider the global optimization of nonconvex mixed-integer quadratic programs with linear equality constraints. In particular, we present a new class of convex quadratic relaxations which are derived via quadratic cuts. To construct these quadrat
Externí odkaz:
http://arxiv.org/abs/2106.13721
Autor:
Ha, Ji Woo, Liu, Junli, Feng, Hao, Sahinidis, Nikolaos V., Seo, Hyerin, Siirola, Jeffrey J., Na, Jonggeol
Publikováno v:
In Cell Reports Physical Science 21 February 2024 5(2)
Publikováno v:
Statist. Sci. Volume 35, Number 4 (2020), 593-601
Sparse linear regression is a vast field and there are many different algorithms available to build models. Two new papers published in Statistical Science study the comparative performance of several sparse regression methodologies, including the la
Externí odkaz:
http://arxiv.org/abs/2011.09362
We consider the global optimization of nonconvex quadratic programs and mixed-integer quadratic programs. We present a family of convex quadratic relaxations which are derived by convexifying nonconvex quadratic functions through perturbations of the
Externí odkaz:
http://arxiv.org/abs/2010.04822
Autor:
Hubbs, Christian D., Perez, Hector D., Sarwar, Owais, Sahinidis, Nikolaos V., Grossmann, Ignacio E., Wassick, John M.
Reinforcement learning (RL) has been widely applied to game-playing and surpassed the best human-level performance in many domains, yet there are few use-cases in industrial or commercial settings. We introduce OR-Gym, an open-source library for deve
Externí odkaz:
http://arxiv.org/abs/2008.06319
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