In today's big data age, while the predict-then-optimize framework has become a standard method for tackling uncertain optimization challenges using machine learning tools, many prediction models overlook data intricacies such as outliers and heteroskedasticity. These oversights can degrade decision-...
When we get more than one optimal solution, there may be a degenerate situation, that is, the predicted values of c will all become 0. To prevent this, we have the definition 2. Proposition 1 illustrates that binary classification is a special case of the SPO framework. The prediction mode...
Numerical experiments on shortest-path and portfolio-optimization problems show that the SPO framework can lead to significant improvement under the predict-then-optimize paradigm, in particular, when the prediction model being trained is misspecified. We find that linear models trained using SPO+ loss...
Fine-tuning the XGBoost model XGBoost2.54%0.45 NN-SPO+2.40%2.04 DFF(ours)2.39%0.49 Fine-tuning the simulation model Average allocation3.45%/ Simulation model2.79%2.59 \times 10^{-2} DFF(ours)2.11%2.61 \times 10^{-2} Table 3: DFFperformanceon different backbone models ...
We consider the use of decision trees for decision-making problems under the predict-then-optimize framework. That is, we would like to first use a decision tree to predict unknown input parameters of an optimization problem, and then make decisions by solving the optimization problem using the ...
The predict-then-optimize framework is fundamental in many practical settings: predict the unknown parameters of an optimization problem, and then solve the problem using the predicted values of the parameters. A natural loss function in this environment is to consider the cost of the decisions ...
To this end, this paper proposes a real-time power system optimization method based on the Smart "Predict, then Optimize" (SPO) framework. The SPO method first uses the Transformer model to predict, in real time, the future line damage states and then dynamically adjusts the optimization ...