Convergence guarantees for forward gradient descent in the linear regression model

Thijs Bos, Johannes Schmidt-Hieber*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

3 Downloads (Pure)


Renewed interest in the relationship between artificial and biological neural networks motivates the study of gradient-free methods. Considering the linear regression model with random design, we theoretically analyze in this work the biologically motivated (weight-perturbed) forward gradient scheme that is based on random linear combination of the gradient. If d denotes the number of parameters and k the number of samples, we prove that the mean squared error of this method converges for k≳d2log(d) with rate d2log(d)/k. Compared to the dimension dependence d for stochastic gradient descent, an additional factor dlog(d) occurs.

Original languageEnglish
Article number106174
JournalJournal of statistical planning and inference
Publication statusPublished - Dec 2024


  • Convergence rates
  • Estimation
  • Gradient descent
  • Linear model
  • Zeroth-order methods


Dive into the research topics of 'Convergence guarantees for forward gradient descent in the linear regression model'. Together they form a unique fingerprint.

Cite this