Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Iterative Linearized Control: Stable Algorithms and Complexity Guarantees
Authors: Vincent Roulet, Siddhartha Srinivasa, Dmitriy Drusvyatskiy, Zaid Harchaoui
ICML 2019 | Venue PDF | LLM Run Details | Input Tokens: 17,966 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,544 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 5. Experiments We illustrate the performance of the algorithms considered including the proposed accelerated regularized Gauss Newton algorithm on two classical problems drawn from Li & Todorov (2004): swing-up a pendulum, and move a twolinks robot arm. |
| Researcher Affiliation | Academia | 1Department of Statistics, University of Washington, Seattle, USA 2Paul G. Allen School of Computer Science & Engineering, University of Washington, Seattle, USA 3Department of Mathematics, University of Washington, Seattle, USA. |
| Pseudocode | Yes | Algorithm 1 Accelerated Regularized Gauss-Newton |
| Open Source Code | Yes | The code for this project is available at https://github.com/vroulet/ilqc. |
| Open Datasets | No | The paper refers to 'inverted pendulum' and 'twolinks arm' as classical problems drawn from Li & Todorov (2004), but it does not provide concrete access information (link, DOI, repository, or specific citation for a dataset file) for any publicly available or open dataset used in the experiments. |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) for training, validation, or testing. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper mentions Py Torch and Tensor Flow but does not specify their version numbers or any other key software components with specific versions needed for replication. |
| Experiment Setup | Yes | For ILQR, we use an Armijo line-search to compute the next step. For both the regularized ILQR and the accelerated regularized ILQR, we use a constant step-size sequence tuned after a burn-in phase of 5 iterations. |