Online Inventory Problems: Beyond the i.i.d. Setting with Online Convex Optimization
Authors: Massil HIHAT, Stéphane Gaïffas, Guillaume Garrigos, Simon Bussy
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, in Section 6 we present numerical experiments on both synthetic and real-world data that validate empirically the versatility and performances of Max COSD. |
| Researcher Affiliation | Collaboration | 1LOPF, Califrais Machine Learning Lab, Paris, France 2Université Paris Cité and Sorbonne Université, CNRS, Laboratoire de Probabilités, Statistique et Modélisation, Paris, France |
| Pseudocode | Yes | Algorithm 2: Max COSD |
| Open Source Code | Yes | The code is available at https://github.com/Califrais/newsvendor_tester. |
| Open Datasets | Yes | Demands are taken from the real-world dataset of the M5 competition [17]. |
| Dataset Splits | No | The paper mentions using the M5 competition dataset for T = 1969 periods but does not provide specific details on training, validation, or test splits. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU or CPU models, or memory specifications used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., Python version, library versions like PyTorch or scikit-learn). |
| Experiment Setup | Yes | All the algorithms have been initialized with y1 = 0. Settings 1, 2 and 3 have been run 10 times, with different demand realizations generated through independent samples. Figure 1 shows, for every setting, the regret obtained after T periods as a function of the learning rate parameter γ [10−5, 101]. We picked T = 1969 for all the settings... |