Efficient Algorithms for Learning Revenue-Maximizing Two-Part Tariffs
Authors: Maria-Florina Balcan, Siddharth Prasad, Tuomas Sandholm
IJCAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We ran Gurobi (the fastest general-purpose mixed integer program solver) to find the revenue-maximizing single TPT for a single buyer (after formulating this problem as an integer program), and Algorithm 1 beat it dramatically. For example, averaged over 10 runs on randomly generated instances with K = 5 units and N = 600 samples, our algorithm returned the revenue-maximizing TPT in under 23 minutes while Gurobi took over 3.5 hours. |
| Researcher Affiliation | Collaboration | Maria-Florina Balcan1 , Siddharth Prasad1 and Tuomas Sandholm1,2,3,4 1School of Computer Science, Carnegie Mellon University 2Optimized Markets, Inc. 3Strategy Robot, Inc. 4Strategic Machine, Inc. |
| Pseudocode | Yes | Algorithm 1 Single TPT for a Single Buyer |
| Open Source Code | No | The paper does not contain any explicit statements about releasing source code or provide links to code repositories. |
| Open Datasets | No | The paper mentions using "randomly generated instances" for experiments but does not provide concrete access information (like a link, DOI, or formal citation) for a publicly available or open dataset. |
| Dataset Splits | No | The paper refers to a "set of samples S" and drawing fresh samples but does not specify explicit training, validation, or test dataset splits (e.g., percentages, sample counts, or specific predefined splits with citations). |
| Hardware Specification | No | The paper mentions running Gurobi but does not specify any hardware details (e.g., CPU/GPU models, memory, or cloud instance types) used for the experiments. |
| Software Dependencies | No | The paper mentions "Gurobi" as a solver but does not specify its version number or any other software dependencies with version information. |
| Experiment Setup | No | The paper mentions parameters for randomly generated instances (K=5 units, N=600 samples) but does not provide specific experimental setup details such as hyperparameters, optimizer settings, or other system-level training configurations for the algorithms themselves. |