Solving complex mixed-binary constrained optimization problems
Keywords:
mixed-binary optimization, energy, quantum computing, efficiency, environmental sustainabilityAbstract
This paper presents the Alternating Direction Method of Multipliers (ADMM) optimizer, a powerful tool for solving optimization problems, especially in the context of mixed-binary constrained optimization (MBCO). This type of problem is widely applied in various fields, including logistics, finance, and operations. The energy industry faces optimization challenges due to the need to maximize energy production efficiency, reduce greenhouse gas emissions, and increase the use of renewable energy. The transition to quantum computing opens up new opportunities for solving these problems, allowing us to process large amounts of data and perform complex calculations more efficiently. The ADMM optimizer is an innovative approach to solving energy optimization problems using quantum computing. It enables us to efficiently solve MBCO problems, taking into account various constraints and requirements associated with energy production and distribution. The results of the study demonstrate the superior performance and accuracy of the ADMM optimizer compared to other optimization methods. The developed ADMM optimizer represents a significant contribution to the field of optimization in the energy industry and has the potential to be widely applied in solving various problems related to energy production and management.
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