In this paper, a new method for solving a mathematical programming problem with linearly complementarity constraints (MPLCC) is introduced, which applies the Levenberg-Marquardt (L-M) method to solve the B-stationary condition of original problem. Under the MPEC-LICQ, the proposed method is proved convergent to B-stationary point of MPLCC.

Let D1 ⊂ D2 be domains in C. Under very mild conditions on D2 we show that there exist holomorphic functions f , defined on D1 with the property that f is nowhere extendible across ∂D1, while the graph of f over D1 is not complete pluripolar in D2 × C. This refutes a conjecture of Levenberg, Martin and Poletsky [6].

We give the first examples of positive closed currents T in C with continuous potentials, T ∧ T = 0 and whose supports do not contain any holomorphic disk. This gives in particular an affirmative answer to a question of Fornæss and Levenberg. We actually construct examples with potential of class C for all α < 1. This regularity is expected to be essentially optimal.

The speed of the Levenberg–Marquardt ~LM! nonlinear iterative least-squares method depends upon the choice of damping strategy when the fitted parameters are highly correlated. Additive damping with small damping increments and large damping decrements permits LM to efficiently solve difficult problems, including those that otherwise cause stagnation. © 1997 American Institute of Physics. @S089...

Abstract—Levenberg-Marquardt method (LM) was proposed to be applied as a non-linear least-square fitting in the analysis of a natural gamma-ray spectrum that was taken by the Hp (Ge) detector. The Gaussian function that composed of three components, main Gaussian, a step background function and tailing function in the lowenergy side, has been suggested to describe each of the -ray lines mathem...

Resumen: En este trabajo se presentan los resultados experimentales del control neuronal de velocidad de un servosistema. La estrategia de control neuronal implementada es el control por modelo inverso. El entrenamiento de la red se ha realizado usando dos algoritmos de aprendizaje: Levenberg-Marquardt y regularización Bayesiana. Se evalúa la capacidad de generalización de cada método en funció...