Near optimal finite time identification of arbitrary linear dynamical systems
Authors: Tuhin Sarkar, Alexander Rakhlin
ICML 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We derive finite time error bounds for estimating general linear time-invariant (LTI) systems from a single observed trajectory using the method of least squares. We provide the first analysis of the general case when eigenvalues of the LTI system are arbitrarily distributed in three regimes: stable, marginally stable, and explosive. Our analysis yields sharp upper bounds for each of these cases separately. |
| Researcher Affiliation | Academia | 1Department of Electrical Engineering and Computer Sciences, MIT 2Department of Brain and Cognitive Sciences, MIT. Correspondence to: Tuhin Sarkar <tsarkar@mit.edu>. |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access to source code for the methodology described, nor does it state that code is released or available. |
| Open Datasets | No | The paper presents theoretical analysis and does not involve empirical studies with specific datasets for training or evaluation. Thus, there is no information regarding public or open datasets. |
| Dataset Splits | No | The paper focuses on theoretical derivations and does not describe empirical experiments, therefore no dataset split information (training, validation, test) is provided. |
| Hardware Specification | No | The paper is theoretical and focuses on mathematical derivations and proofs. It does not describe any empirical experiments, and therefore no specific hardware specifications are mentioned. |
| Software Dependencies | No | The paper is a theoretical work and does not describe empirical experiments requiring specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe any empirical experiments, therefore no specific experimental setup details, hyperparameters, or training configurations are provided. |