Solving complex problems of mixed-binary bounded optimization based on quantum algorithms
Abstract
The paper considers the ADMM (Alternating Direction Method of Multipliers) optimizer, which is a powerful tool for solving complex optimization problems, especially in the context of mixed-binary constrained optimization (MBCO). This study presents a new ADMM optimizer that demonstrates efficiency in solving optimization problems in the field of energy. The developed ADMM optimizer represents an innovative approach to solving optimization problems in the field of energy using quantum computing. It allows you to effectively solve MBCO tasks, taking into account various constraints and requirements related to energy production and distribution. The results of the study demonstrate the excellent performance and accuracy of the ADMM optimizer in comparison with other optimization methods. The developed ADMM optimizer represents a significant contribution to the field of optimization in the energy industry and has the potential for wide application in solving various tasks related to energy production and management.