Wasserstein Distributionally Robust Inverse Multiobjective Optimization
Authors: Chaosheng Dong, Bo Zeng5914-5921
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we demonstrate the effectiveness of our method on both a synthetic multiobjective quadratic program and a real world portfolio optimization problem. Experiments In this section, we provide an MQP and a portfolio optimization problem to illustrate the performance of Algorithm 1. |
| Researcher Affiliation | Collaboration | Chaosheng Dong1*, Bo Zeng2 1Amazon 2University of Pittsburgh chaosd@amazon.com, bzeng@pitt.edu |
| Pseudocode | Yes | Algorithm 1 Wasserstein Distributionally Robust IMOP |
| Open Source Code | No | The paper states 'All the algorithms are programmed with Julia (Bezanson et al. 2017)' but does not provide a link or explicit statement about the availability of the source code for the described methodology. |
| Open Datasets | No | The paper describes how synthetic data and real-world case study data are generated/derived ('We first compute Pareto optimal solutions {xi}i [N] by solving WP with weight samples {wi}i [N] that are uniformly chosen from W2. Next, the noisy decision yi is obtained by adding noise to xi for each i [N].' and 'The dataset is derived from monthly total returns of 30 stocks from a blue-chip index...'). It refers to 'supplementary material' for 'true expected returns and true return covariance matrix', but does not provide a concrete link or citation to an openly accessible public dataset with author attribution or a repository for direct download. |
| Dataset Splits | Yes | Here, we use an independent validation set that consists of 105 noisy decisions generated in the same way as the training data to compute the prediction error. |
| Hardware Specification | No | No specific hardware details (e.g., GPU models, CPU types, or memory) are provided. |
| Software Dependencies | No | The paper mentions 'Gurobi' and 'Julia' but does not specify their version numbers. It also refers to 'Baron (Sahinidis 1996)' without a version. |
| Experiment Setup | Yes | K = 6 weights from W2 are evenly sampled. The radius ϵ of the Wasserstein ambiguity set is selected from the set {10 4, 10 3, 10 2, 10 1, 1}. The stopping criteria δ is set to be 0.1. |