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pro vyhledávání: '"Smith, Neil P."'
Modern Mixed Integer Linear Programming (MILP) solvers use the Branch-and-Bound algorithm together with a plethora of auxiliary components that speed up the search. In recent years, there has been an explosive development in the use of machine learni
Externí odkaz:
http://arxiv.org/abs/2411.18321
Mixed Integer Linear Programming (MILP) is a pillar of mathematical optimization that offers a powerful modeling language for a wide range of applications. During the past decades, enormous algorithmic progress has been made in solving MILPs, and man
Externí odkaz:
http://arxiv.org/abs/2402.05501
Robust Optimal Control (ROC) with adjustable uncertainties has proven to be effective in addressing critical challenges within modern energy networks, especially the reserve and provision problem. However, prior research on ROC with adjustable uncert
Externí odkaz:
http://arxiv.org/abs/2312.11251
Towards integrating renewable electricity generation sources into the grid, an important facilitator is the energy flexibility provided by buildings' thermal inertia. Most of the existing research follows a single-step price- or incentive-based schem
Externí odkaz:
http://arxiv.org/abs/2312.05108
ReLU neural networks have been modelled as constraints in mixed integer linear programming (MILP), enabling surrogate-based optimisation in various domains and efficient solution of machine learning certification problems. However, previous works are
Externí odkaz:
http://arxiv.org/abs/2312.01228
Publikováno v:
Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence, IJCAI-24, pages 4868-4875, 2024
Optimization models used to make discrete decisions often contain uncertain parameters that are context-dependent and estimated through prediction. To account for the quality of the decision made based on the prediction, decision-focused learning (en
Externí odkaz:
http://arxiv.org/abs/2310.04328
Agent-based modelling constitutes a versatile approach to representing and simulating complex systems. Studying large-scale systems is challenging because of the computational time required for the simulation runs: scaling is at least linear in syste
Externí odkaz:
http://arxiv.org/abs/2304.01724
Autor:
Bernardelli, Ambrogio Maria, Gualandi, Stefano, Lau, Hoong Chuin, Milanesi, Simone, Yorke-Smith, Neil
Training neural networks (NNs) using combinatorial optimization solvers has gained attention in recent years. In low-data settings, state-of-the-art mixed integer linear programming solvers can train exactly a NN, avoiding intensive GPU-based trainin
Externí odkaz:
http://arxiv.org/abs/2212.03659
Autor:
Scavuzzo, Lara, Chen, Feng Yang, Chételat, Didier, Gasse, Maxime, Lodi, Andrea, Yorke-Smith, Neil, Aardal, Karen
State-of-the-art Mixed Integer Linear Program (MILP) solvers combine systematic tree search with a plethora of hard-coded heuristics, such as the branching rule. The idea of learning branching rules from data has received increasing attention recentl
Externí odkaz:
http://arxiv.org/abs/2205.11107
Autor:
Teso, Stefano, Bliek, Laurens, Borghesi, Andrea, Lombardi, Michele, Yorke-Smith, Neil, Guns, Tias, Passerini, Andrea
It is increasingly common to solve combinatorial optimisation problems that are partially-specified. We survey the case where the objective function or the relations between variables are not known or are only partially specified. The challenge is to
Externí odkaz:
http://arxiv.org/abs/2205.10157