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.

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  1. Terzakis, George; Lourakis, Manolis; Ait-Boudaoud, Djamel: Modified Rodrigues parameters: an efficient representation of orientation in 3D vision and graphics (2018)
  2. Cui, Yiran; del Baño Rollin, Sebastian; Germano, Guido: Full and fast calibration of the Heston stochastic volatility model (2017)
  3. Schumann-Bischoff, Jan; Luther, Stefan; Parlitz, Ulrich: Nonlinear system identification employing automatic differentiation (2013)
  4. 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
  5. Lourakis, Manolis I. A.: Sparse non-linear least squares optimization for geometric vision (2010) ioport