We introduce and study online conversion with switching costs, a family of
online problems that capture emerging problems at the intersection of energy
and sustainability. In this problem, an online player attempts to purchase
(alternatively, sell) fractional shares of an asset during a fixed time horizon
with length $T$. At each time step, a cost function (alternatively, price
function) is revealed, and the player must irrevocably decide an amount of
asset to convert. The player also incurs a switching cost whenever their
decision changes in consecutive time steps, i.e., when they increase or
decrease their purchasing amount. We introduce competitive (robust)
threshold-based algorithms for both the minimization and maximization variants
of this problem, and show they are optimal among deterministic online
algorithms. We then propose learning-augmented algorithms that take advantage
of untrusted black-box advice (such as predictions from a machine learning
model) to achieve significantly better average-case performance without
sacrificing worst-case competitive guarantees. Finally, we empirically evaluate
our proposed algorithms using a carbon-aware EV charging case study, showing
that our algorithms substantially improve on baseline methods for this problem.

介绍并研究了涉及能源和可持续发展领域的新兴问题的在线转换与切换成本，通过引入在线玩家尝试在固定时间范围内购买（或销售）资产的问题，我们提出了基于阈值的竞争（鲁棒）算法来优化解决该问题，同时还提出了利用不可信黑盒建议（例如机器学习模型的预测）的学习增强算法，以在平均情况下显著提高性能并保证最坏情况下的竞争性能，并通过碳感知的电动汽车充电案例研究对所提出的算法进行了实证评估，结果显示相对基准方法，我们的算法显著改进了该问题的解决方法。