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Levenberg-Marquardt Algorithms for Nonlinear Inverse Least Squares
Vyacheslav Kungurtsev
Department of Computer Science, Czech Technical University, Prague.
Abstract:
Levenberg-Marquardt (LM) algorithms are a class of methods that add a regularization term to a Gauss-Newton method to promote better convergence properties. This talk presents three works on this class of methods. The first discusses a new method that simultaneously achieves all types of state of the art convergence guarantees for unconstrained problems. Stochastic LM is discussed next, which is an algorithm to handle noisy data. An example is presented on data assimilation. Finally, a LM method is presented to handle equality constraints, with examples from inverse problems in PDEs.
Tuesday, February 23, 2021
11:00AM Zoom Meeting ID: 950 6794 9984
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Randolph E. Bank
Philip E. Gill
Michael Holst
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Randolph E. Bank
Philip E. Gill
Michael Holst
Administrative Contact:
[ Click to Send Email ]
Vyacheslav Kungurtsev
Department of Computer Science, Czech Technical University, Prague.
Abstract:
Levenberg-Marquardt (LM) algorithms are a class of methods that add a regularization term to a Gauss-Newton method to promote better convergence properties. This talk presents three works on this class of methods. The first discusses a new method that simultaneously achieves all types of state of the art convergence guarantees for unconstrained problems. Stochastic LM is discussed next, which is an algorithm to handle noisy data. An example is presented on data assimilation. Finally, a LM method is presented to handle equality constraints, with examples from inverse problems in PDEs.
Tuesday, February 23, 2021
11:00AM Zoom Meeting ID: 950 6794 9984