具有非形回归函数的双阶段存量程序的分解算法
1Department of Industrial Engineering and Operations Research, University of California, Berkeley, Berkeley, CA 94720 USA.
概括
这项研究引入了一种新的分解方法来解决复杂的二阶段随机程序. 这种新方法有效地处理非凸性,为具有挑战性的优化问题提供了强有力的解决方案.
科学领域:
- 优化情况
- 数学编程
- 计算科学
背景情况:
- 经典的分解方法在两个阶段的随机程序中失败.
- 通过第一阶段变量进行非线性参数化复杂化了第二阶段的目标和约束.
- 克拉克规律性失误阻止了德或拉格朗的算法的直接通用化.
研究的目的:
- 为非凸的两阶段随机程序开发一种新的分解框架.
- 解决当克拉克规律性失败时现有方法的局限性.
- 为非线性参数化的第二阶段求助函数提供有效的算法.
主要方法:
- 探索隐含的凸形结构的求助函数.
- 引入使用部分莫罗包的分解框架.
- 连续生成强的二次方程近似函数.
- 通过解决第二阶段凸子问题的方法将近值纳入第一阶段的主题.
主要成果:
- 在固定和顺序采样场景中确定了趋同.
- 通过数值实验证明拟议的算法的有效性.
- 在二阶段随机编程中成功解决了非凸度的挑战.
结论:
- 新的部分莫罗包式分解框架对于非凸的双阶段随机程序是有效的.
- 该方法克服了经典分解算法的局限性.
- 该方法为具有非线性参数化的求助函数的复杂优化问题提供了可行的解决方案.
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