论文的作者是Elmachtoub@Columbia University和Grigas@UCB,研究介绍了一种新的优化框架SPO,该框架将传统的两阶段解决问题的范式(预测、优化)合并为一个步骤,称之为Smart "Predict, then Optimize"。SPO框架最大的特点是利用了优化问题的结构(目标函数和约束条件),其最关键的部分是SPO损失函数,该损失函数度量了预测信...
A new framework for developing prediction models under the predict-then-optimize paradigm.Relies on new types of loss functions that explicitly incorporate the problem structure of the optimization problem of interest.SPO+ loss function performs well in comparsion to standard predict-then-optimize approa...
This work introduces thePyEPOpackage, aPyTorch-based end-to-end predict-then-optimize library in Python. To the best of our knowledge,PyEPO(pronounced likepineapplewith a silent "n") is the first such generic tool for linear and integer programming with predicted objective function coefficients....
Smart “predict, then optimize”. Management Science, 2022, 68(1): 9–26 Google Scholar Mandi J, Demirović E, Stuckey P J, et al. Smart predict-and-optimize for hard combinatorial optimization problems. Proceedings of the AAAI Conference on Artificial Intelligence, 2020, 34(02): 1603–...
Imagine we have a feedback function (like a reward function) that tells us how well the agent is doing. We can then optimize this agent online: fromopto.optimizersimportOptoPrimedeffeedback_fn(generated_response,gold_label='en'):ifgold_label=='en'and'Hello'ingenerated_response:return"Correct...
For the manufacturing sector, real-time data from machinery and production lines can significantly enhance operational efficiency. The combined power of ThingsBoard and OCI helps ensure that sensor data is promptly analyzed to detect anomalies, predict maintenance needs, and optimize production processes....
To eliminate the coplanar assumption, we could first cal- culate the gaze origin and then take the face/eye crop to predict the gaze direction as a two-step approach. To ob- tain the gaze origin and face/eye crop, data normalization is proposed as a pre-processing step [29, 40]. As...
Three-dimensional structures of protein–ligand complexes provide valuable insights into their interactions and are crucial for molecular biological studies and drug design. However, their high-dimensional and multimodal nature hinders end-to-end modelin
Predict, then Optimize框架 问题场景的假设:目标函数为线性,决策变量和约束条件有着明确的定义(well defined and known with certainty), 但是问题中的目标函数的cost vector是未知的,但是可以通过问题实例的特征数据进行预测。 Specifically, a prediction (machine learning) model is used that maps the feature vect...
2.Predict-then-Optimize:使用线性预测层的两阶段系统,没有参数是通过学习得到的 3.Base:没有加入risk function的End-to-end系统,使用线性预测层,唯一可学习的参数是 θ ,该系统相当于结果可变性不受决策变量 zt 影响的系统[1]。 4.Nominal:End-to-End系统具备线性预测层和考虑名义问题的决策层,可学习参数为 ...