Proximal gradient descent and its accelerated version are resultful methods for solving the sum of smooth and non-smooth problems. When the smooth function can be represented as a sum of multiple functions, the stocha...Proximal gradient descent and its accelerated version are resultful methods for solving the sum of smooth and non-smooth problems. When the smooth function can be represented as a sum of multiple functions, the stochastic proximal gradient method performs well. However, research on its accelerated version remains unclear. This paper proposes a proximal stochastic accelerated gradient (PSAG) method to address problems involving a combination of smooth and non-smooth components, where the smooth part corresponds to the average of multiple block sums. Simultaneously, most of convergence analyses hold in expectation. To this end, under some mind conditions, we present an almost sure convergence of unbiased gradient estimation in the non-smooth setting. Moreover, we establish that the minimum of the squared gradient mapping norm arbitrarily converges to zero with probability one.展开更多
在考虑风电出力不确定性的基础上研究了多区域电网分散式优化问题。首先,对区域边界共享节点进行复制以实现互联电网的解耦,然后,基于场景法对风电随机性进行建模,构建区域电网两阶段随机优化模型,最后,采用同步型交替方向乘子法(Synchr...在考虑风电出力不确定性的基础上研究了多区域电网分散式优化问题。首先,对区域边界共享节点进行复制以实现互联电网的解耦,然后,基于场景法对风电随机性进行建模,构建区域电网两阶段随机优化模型,最后,采用同步型交替方向乘子法(Synchronous Alternating Direction Method of Multipliers,SADMM)交替求解全网分散优化问题和区域两阶段随机优化问题。采用修改的新英格兰39节点系统构建3区域互联电网进行仿真测试,验证了所提模型能够有效应对风电的随机不确定性,并实现各区域电网调度的分散自治。展开更多
文摘Proximal gradient descent and its accelerated version are resultful methods for solving the sum of smooth and non-smooth problems. When the smooth function can be represented as a sum of multiple functions, the stochastic proximal gradient method performs well. However, research on its accelerated version remains unclear. This paper proposes a proximal stochastic accelerated gradient (PSAG) method to address problems involving a combination of smooth and non-smooth components, where the smooth part corresponds to the average of multiple block sums. Simultaneously, most of convergence analyses hold in expectation. To this end, under some mind conditions, we present an almost sure convergence of unbiased gradient estimation in the non-smooth setting. Moreover, we establish that the minimum of the squared gradient mapping norm arbitrarily converges to zero with probability one.
文摘在考虑风电出力不确定性的基础上研究了多区域电网分散式优化问题。首先,对区域边界共享节点进行复制以实现互联电网的解耦,然后,基于场景法对风电随机性进行建模,构建区域电网两阶段随机优化模型,最后,采用同步型交替方向乘子法(Synchronous Alternating Direction Method of Multipliers,SADMM)交替求解全网分散优化问题和区域两阶段随机优化问题。采用修改的新英格兰39节点系统构建3区域互联电网进行仿真测试,验证了所提模型能够有效应对风电的随机不确定性,并实现各区域电网调度的分散自治。
