# Separable nonlinear least-squares parameter estimation for complex dynamic systems

arXiv: https://arxiv.org/abs/1908.03717 · HTML: https://mlsanity.com/paper/1908.03717/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Aug 2019.

## Built on by (most cited first, 1 of 1)

- [Reconstruction and Prediction of Volterra Integral Equations Driven by Gaussian Noise](https://mlsanity.com/paper/2506.00933/) (2025)

## Abstract (arXiv)

Nonlinear dynamic models are widely used for characterizing functional forms of processes that govern complex biological pathway systems. Over the past decade, validation and further development of these models became possible due to data collected via high-throughput experiments using methods from molecular biology. While these data are very beneficial, they are typically incomplete and noisy, so that inferring parameter values for complex dynamic models is associated with serious computational challenges. Fortunately, many biological systems have embedded linear mathematical features, which may be exploited, thereby improving fits and leading to better convergence of optimization algorithms. In this paper, we explore options of inference for dynamic models using a novel method of it separable nonlinear least-squares optimization, and compare its performance to the traditional nonlinear least-squares method. The numerical results from extensive simulations suggest that the proposed approach is at least as accurate as the traditional nonlinear least-squares, but usually superior, while also enjoying a substantial reduction in computational time.