Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Continuation Path Learning for Homotopy Optimization
Authors: Xi Lin, Zhiyuan Yang, Xiaoyuan Zhang, Qingfu Zhang
ICML 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental studies on different problems show that our proposed method can significantly improve the performance of homotopy optimization and provide extra helpful information to support better decision-making. |
| Researcher Affiliation | Academia | 1Department of Computer Science, City University of Hong Kong. Correspondence to: Xi Lin <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 Classical Homotopy Optimization Algorithm |
| Open Source Code | Yes | The source code can be found in https://github.com/ Xi-L/CPL. |
| Open Datasets | Yes | We first test CPL s performance on three widely-used synthetic test benchmark problems, namely the Ackley function (Ackley, 1987), the Rosenbrock function (Rosenbrock, 1960), and the Himmelblau function (Himmelblau et al., 1972). |
| Dataset Splits | No | The paper mentions generating training data and using separate test data for evaluation, but does not explicitly specify the use of a validation dataset split (e.g., 80/10/10 split or specific counts for training, validation, and test sets). |
| Hardware Specification | Yes | The CPL training on GPU (RTX-3080) is actually slower than its counterpart on CPU. |
| Software Dependencies | No | The paper mentions using "Py Torch" and building on the "POMO codebase", but does not provide specific version numbers for these or other software dependencies. |
| Experiment Setup | Yes | The optimizer we use is Adam with learning rate η = 10 4, weight decay ω = 10 6 and batch size B = 64. At each training epoch, we randomly generate 100, 000 problem instances on the fly as training data, and train the model for 1, 000 epoch. |