Nonnegative Tensor Completion via Integer Optimization
Authors: Caleb Bugg, Chen Chen, Anil Aswani
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Here we present results that show the efficacy and scalability of our algorithm for nonnegative tensor completion. Our experiments were conducted on a laptop computer... |
| Researcher Affiliation | Academia | University of California, Berkeley, {caleb_bugg,aaswani}@berkeley.edu The Ohio State University, chen.8018@osu.edu |
| Pseudocode | Yes | Algorithm 1: Weak Separation Oracle for Cλ; Algorithm 2: Alternating Maximization |
| Open Source Code | Yes | There is new code in the Supplemental Material. |
| Open Datasets | No | The paper does not use a publicly available or open dataset. Instead, the true tensor was constructed for the experiments: 'In each experiment, the true tensor ψ was constructed by randomly choosing 10 points from S1 and then taking a random convex combination.' |
| Dataset Splits | No | The paper does not explicitly state dataset splits for training, validation, or testing. It mentions using 'n samples' for tensor completion. |
| Hardware Specification | Yes | Our experiments were conducted on a laptop computer with 8GB of RAM and an Intel Core i5 2.3Ghz processor with 2-cores/4-threads. |
| Software Dependencies | Yes | The algorithms were coded in Python 3. We used Gurobi v9.1 (Gurobi Optimization, LLC, 2021) to solve the integer programs (13). |
| Experiment Setup | Yes | To minimize the impact of hyperparameter selection in our numerical results, we provided the ground truth values when possible. For instance, in our nonnegative tensor completion formulation (8) we chose λ to be the smallest value for which we could certify that ψ + λ for the true tensor ψ. This was accomplished by construction of the true tensor ψ. For ALS and TNCP, we used a nonnegative rank k that was the smallest value for which we could certify that rank+(ψ) k. |