Skip to content
DraconiticResearch
Search Sources API
203,203 studies31 sourcesUpdated 32 min ago
PreprintarXiv licencearXiv

Deterministically Optimal Robust Exact Differentiators of Arbitrary Order

Abstract

Estimation of the derivatives of a function with bounded high-order derivative in the presence of bounded measurement noise is considered, in a deterministic setting. Theoretical fundamental limitations of causal differentiators in terms of the lowest achievable worst-case differentiation error and desired properties such as exactness---the zero-error estimation of derivatives in the absence of noise---and robustness---the small sensitivity of the estimate under small perturbations---are established for the first time for arbitrary differentiation order. Differentiators that achieve these theoretically lowest bounds on their differentiation error are formally defined, fully characterized, and their properties are studied. In particular, deterministically optimal differentiators---those featuring optimal differentiation error bounds among the class of exact differentiators---are shown to exist by means of a novel construction exhibiting, in addition to exactness from the beginning, a very strong form of robustness.

Related

  1. Exact resummation of hydrodynamics for arbitrary elastic scattering integral
    Lorenzo Gavassino
    arXivPreprintarXiv licenceFull textMathematical physicsHigh-energy & particle physicsarXiv
  2. Optimal Multiclass Online Regret with Arbitrary Label Spaces
    Sajad Ashkezari, Shai Ben-David
    arXivPreprintCC BY 4.0Full textStatisticsarXiv
  3. Arbitrary-Accuracy Neural Approximation with Optimal Neuron Count and Near-Optimal Bit Complexity
    Zilan Cheng, Li-Lian Wang, Zhongjian Wang
    arXivPreprintCC BY-SA 4.0Full textStatisticsarXiv
  4. Optimal Convergence of Quadratic Not-a-Knot Spline Interpolation on Arbitrary Partitions
    Aussie Greene
    arXivPreprintarXiv licenceFull textNumerical analysis & scientific computingarXiv
  5. Optimal Learning in Games with Arbitrary Delays: Best-of-Both-Worlds for Self-Play and Adversarial Opponents
    Emmanuel Esposito, Andrew Jacobsen, Hao Qiu +2
    arXivPreprintarXiv licenceFull textStatisticsarXiv
BibTeX
@misc{seeber2026deterministically,
  title = {{Deterministically Optimal Robust Exact Differentiators of Arbitrary Order}},
  author = {Richard Seeber and Hernan Haimovich},
  howpublished = {arXiv},
  year = {2026},
  month = sep,
  doi = {10.48550/arxiv.2609.15564},
  eprint = {2609.15564},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2609.15564},
}
RIS
TY  - UNPB
TI  - Deterministically Optimal Robust Exact Differentiators of Arbitrary Order
AU  - Richard Seeber
AU  - Hernan Haimovich
PY  - 2026
DA  - 2026/09/14
PB  - arXiv
DO  - 10.48550/arxiv.2609.15564
UR  - https://arxiv.org/abs/2609.15564
AB  - Estimation of the derivatives of a function with bounded high-order derivative in the presence of bounded measurement noise is considered, in a deterministic setting. Theoretical fundamental limitations of causal differentiators in terms of the lowest achievable worst-case differentiation error and desired properties such as exactness---the zero-error estimation of derivatives in the absence of noise---and robustness---the small sensitivity of the estimate under small perturbations---are established for the first time for arbitrary differentiation order. Differentiators that achieve these theoretically lowest bounds on their differentiation error are formally defined, fully characterized, and their properties are studied. In particular, deterministically optimal differentiators---those featuring optimal differentiation error bounds among the class of exact differentiators---are shown to exist by means of a novel construction exhibiting, in addition to exactness from the beginning, a very strong form of robustness.
ER  -