论文标题
ODDO：在线双重驱动优化
ODDO: Online Duality-Driven Optimization
论文作者
论文摘要
由微电网能源管理的促进，我们研究了目标函数和顺序决策的不确定性的凸优化问题。为了解决这些问题，我们提出了一个新框架，称为``在线二元驱动优化''（ODDO）。该框架将自己与现有的范式区分开来，以在其效率，简单性和解决问题的能力上进行优化，而无需对不确定数据进行任何定量假设。在此框架中，关键思想是我们预测最佳的Lagrange乘数而不是实际的不确定数据。随后，我们使用这些预测来利用问题的强双重性来构建在线原理。我们表明，该框架在理论上和实践中都与最佳拉格朗日乘数中的预测错误相对强大。实际上，对框架对实际和随机生成的输入数据的问题的评估表明，即使我们仅使用基本统计信息来预测最佳的Lagrange乘数，ODDO也可以实现近乎最佳的在线解决方案。
Motivated by energy management for micro-grids, we study convex optimization problems with uncertainty in the objective function and sequential decision making. To solve these problems, we propose a new framework called ``Online Duality-Driven Optimization'' (ODDO). This framework distinguishes itself from existing paradigms for optimization under uncertainty in its efficiency, simplicity, and ability to solve problems without any quantitative assumptions on the uncertain data. The key idea in this framework is that we predict, instead of the actual uncertain data, the optimal Lagrange multipliers. Subsequently, we use these predictions to construct an online primal solution by exploiting strong duality of the problem. We show that the framework is robust against prediction errors in the optimal Lagrange multipliers both theoretically and in practice. In fact, evaluations of the framework on problems with both real and randomly generated input data show that ODDO can achieve near-optimal online solutions, even when we use only elementary statistics to predict the optimal Lagrange multipliers.
