Continuation Path Learning for Homotopy Optimization
Authors: Xi Lin, Zhiyuan Yang, Xiaoyuan Zhang, Qingfu Zhang
ICML 2023 | Conference PDF | Archive PDF | Plain Text | 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 <xi.lin@my.cityu.edu.hk>. |
| 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. |