Parallel inexact Levenberg–Marquardt method for nearly-separable nonlinear least squares

Lidija Fodor (), Dušan Jakovetić (), Nataša Krejić () and Greta Malaspina ()
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Lidija Fodor: University of Novi Sad
Dušan Jakovetić: University of Novi Sad
Nataša Krejić: University of Novi Sad
Greta Malaspina: Università degli studi di Firenze

Journal of Global Optimization, 2025, vol. 92, issue 3, No 6, 663-691

Abstract: Abstract Motivated by localization problems such as cadastral maps refinements, we consider a generic Nonlinear Least Squares (NLS) problem of minimizing an aggregate squared fit across all nonlinear equations (measurements) with respect to the set of unknowns, e.g., coordinates of the unknown points’ locations. In a number of scenarios, NLS problems exhibit a nearly-separable structure: the set of measurements can be partitioned into disjoint groups (blocks), such that the unknowns that correspond to different blocks are only loosely coupled. We propose an efficient parallel method, termed Parallel Inexact Levenberg–Marquardt (PILM), to solve such generic large scale NLS problems. PILM builds upon the classical Levenberg–Marquard (LM) method, with a main novelty in that the nearly-block separable structure is leveraged in order to obtain a scalable parallel method. Therein, the problem-wide system of linear equations that needs to be solved at every LM iteration is tackled iteratively. At each (inner) iteration, the block-wise systems of linear equations are solved in parallel, while the problem-wide system is then handled via sparse, inexpensive inter-block communication. We establish strong convergence guarantees of PILM that are analogous to those of the classical LM; provide PILM implementation in a master-worker parallel computational environment; and demonstrate its efficiency on huge scale cadastral map refinement problems.

Keywords: Distributed optimization; Sparse nonlinear least squares; Inexact Levenberg–Marquardt method; Nearly separable problems; Localization problems; Cadastral maps refinement (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s10898-025-01494-5

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