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A convergent decomposition method for box-constrained optimization problems

机译:箱约束优化问题的收敛分解方法

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摘要

In this work we consider the problem of minimizing a continuously differentiable function over a feasible set defined by box constraints. We present a decomposition method based on the solution of a sequence of subproblems. In particular, we state conditions on the rule for selecting the subproblem variables sufficient to ensure the global convergence of the generated sequence without convexity assumptions. The conditions require to select suitable variables (related to the violation of the optimality conditions) to guarantee theoretical convergence properties, and leave the degree of freedom of selecting any other group of variables to accelerate the convergence.
机译:在这项工作中,我们考虑了在由框约束定义的可行集上最小化连续可微函数的问题。我们提出了一种基于一系列子问题的解决方案的分解方法。特别是,我们在选择子问题变量的规则上陈述了条件,这些条件足以确保所生成序列的全局收敛而没有凸度假设。这些条件要求选择合适的变量(与违反最优性条件有关)以保证理论收敛性,并留出选择任何其他变量组的自由度以加快收敛。

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