Victor Pereyra

The differentiation of pseudoinverses and nonlinear least squares problems whose variables separate.

For given data (

Separable nonlinear least squares: the variable projection method and its applications

t_i\ , y_i), i=1, \ldots ,m

Iterative Methods for Solving Nonlinear Least Squares Problems

, we consider the least squares fit of nonlinear models of the form F(

Accelerating the Convergence of Discretization Algorithms

\underset ~\to a\ , \underset ~\to \alpha\ ; t) = \sum_{j=1}^{n}\ g_j (\underset ~\to a ) \varphi_j (\underset ~\to \alpha\ ; t) , \underset ~\to a\ \epsilon R^s\ , \underset ~\to \alpha\ \epsilon R^k\

Aircraft design optimization

. For this purpose we study the minimization of the nonlinear functional r(

A Method for Separable Nonlinear Least Squares Problems with Separable Nonlinear Equality Constraints

\underset ~\to a\ , \underset ~\to \alpha ) = \sum_{i=1}^{m} {(y_i - F(\underset ~\to a , \underset ~\to \alpha , t_i))}^2

Exponential Data Fitting and its Applications

. It is shown that by defining the matrix

Fast computation of equispaced Pareto manifolds and Pareto fronts for multiobjective optimization problems

{ \{\Phi (\underset ~\to \alpha\} }_{i,j} = \varphi_j (\underset ~\to \alpha ; t_i)

Equispaced Pareto front construction for constrained bi-objective optimization

, and the modified functional

Large scale least squares scattered data fitting

r_2(\underset ~\to \alpha ) = \l\ \underset ~\to y\ - \Phi (\underset ~\to \alpha )\Phi^+(\underset ~\to \alpha ) \underset ~\to y \l_2^2