sparseLM : Sparse Levenberg-Marquardt nonlinear least squares in C/C++ Several estimation problems in vision involve the minimization of cumulative geometric error using non-linear least-squares fitting. Typically, this error is characterized by the lack of interdependence among certain subgroups of the parameters to be estimated, which leads to minimization problems possessing a sparse structure. Taking advantage of this sparseness during minimization is known to achieve enormous computational savings. Nevertheless, since the underlying sparsity pattern is problem-dependent, its exploitation for a particular estimation problem requires non-trivial implementation effort, which often discourages its pursuance in practice. Based on recent developments in sparse linear solvers, this paper provides an overview of sparseLM, a general-purpose software package for sparse non-linear least squares that can exhibit arbitrary sparseness and presents results from its application to important sparse estimation problems in geometric vision.
Keywords for this software
References in zbMATH (referenced in 4 articles )
Showing results 1 to 4 of 4.
- Cui, Yiran; del Baño Rollin, Sebastian; Germano, Guido: Full and fast calibration of the Heston stochastic volatility model (2017)
- Schumann-Bischoff, Jan; Luther, Stefan; Parlitz, Ulrich: Nonlinear system identification employing automatic differentiation (2013)
- Weibel, Thomas; Daul, Christian; Wolf, Didier; Rösch, Ronald; Guillemin, François: Graph based construction of textured large field of view mosaics for bladder cancer diagnosis (2012) ioport
- Lourakis, Manolis I. A.: Sparse non-linear least squares optimization for geometric vision (2010) ioport