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pro vyhledávání: '"SMITH, NEIL"'
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