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<p>This paper presents a global image space method for addressing a class of nonconvex programming problems (CNPP). By utilizing equivalent conversion and a two-phase linear relaxation technique, the CNPP can be transformed into a relaxed linear program problem. On the basis of the branch-and-bound framework, a global image space algorithm is designed for addressing the CNPP. By analyzing the computational complexity of this algorithm, we estimate the maximum number of iterations in the worst-case scenario. Finally, numerical experimental results are presented to indicate that this method is practicable and effective.</p>

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An efficient image space algorithm for solving a class of nonconvex programming problems

  • 期刊论文

  • 发表日期：

    2026年3月16日

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  • Kun Hou,
  • Yan Gao

摘要

This paper presents a global image space method for addressing a class of nonconvex programming problems (CNPP). By utilizing equivalent conversion and a two-phase linear relaxation technique, the CNPP can be transformed into a relaxed linear program problem. On the basis of the branch-and-bound framework, a global image space algorithm is designed for addressing the CNPP. By analyzing the computational complexity of this algorithm, we estimate the maximum number of iterations in the worst-case scenario. Finally, numerical experimental results are presented to indicate that this method is practicable and effective.