DOMINO: Data-driven Optimization of bi-level Mixed-Integer NOnlinear Problems
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Summary
Although this data-driven approach cannot provide a theoretical guarantee to global optimality, this work presents an algorithmic advancement that can guarantee feasibility to large-scale bi-level optimization problems when the lower-level problem is solved toglobal optimality at convergence.
- Type
- article
- Published
- 2020-02-18
- Cited by
- 47
- References
- 85
- Access
- Open access
- OpenAlex
- https://openalex.org/W3007441549
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:214324460
Keywords
Mathematical optimization, Global optimization, Solver, Nonlinear programming, Integer programming
References
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- On the Stackelberg strategy in nonzero-sum games
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- Surrogate-based optimization for mixed-integer nonlinear problems
- A data-driven optimization algorithm for differential algebraic equations with numerical infeasibilities
- COBALT: COnstrained Bayesian optimizAtion of computationaLly expensive grey-box models exploiting derivaTive information
- Combining Experimental Isotherms, Minimalistic Simulations, and a Model to Understand and Predict Chemical Adsorption onto Montmorillonite Clays
- An improved assisted evolutionary algorithm for data-driven mixed integer optimization based on Two_Arch
- A reformulation strategy for mixed-integer linear bi-level programming problems
- Bi-level Mixed-Integer Data-Driven Optimization of Integrated Planning and Scheduling Problems
- Integrating process design and control using reinforcement learning
- Simultaneous Process Design and Control Optimization using Reinforcement Learning
- Data-Driven Optimization of Mixed-integer Bi-level Multi-follower Integrated Planning and Scheduling Problems Under Demand Uncertainty
- Data-driven optimization for process systems engineering applications
- Data-driven stochastic optimization for distributional ambiguity with integrated confidence region
- A decision-making framework for the optimal design of renewable energy systems under energy-water-land nexus considerations.
- A Data-Driven Stackelberg Game Approach Applied to Analysis of Strategic Bidding for Distributed Energy Resource Aggregator in Electricity Markets
- Bilevel optimisation with embedded neural networks: Application to scheduling and control integration
- Analysis of strategic bidding of a DER aggregator in energy markets through the Stackelberg game model with the mixed-integer lower-level problem
- Machine Learning Methods for Endocrine Disrupting Potential Identification Based on Single-Cell Data
- A Nash–Stackelberg game approach to analyze strategic bidding for multiple DER aggregators in electricity markets
- Machine Learning for Large-Scale Optimization in 6G Wireless Networks
- A comprehensive classification of food-energy-water nexus optimization studies: State of the art
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