A methodology for solving chemical equilibrium systems
Applied Mathematics and Computation
Chemical equilibrium systems as numerical test problems
ACM Transactions on Mathematical Software (TOMS)
An interior point potential reduction method for constrained equations
Mathematical Programming: Series A and B
Iterative solution of nonlinear equations in several variables
Iterative solution of nonlinear equations in several variables
Test Examples for Nonlinear Programming Codes
Test Examples for Nonlinear Programming Codes
A Potential Reduction Newton Method for Constrained Equations
SIAM Journal on Optimization
An affine scaling trust-region approach to bound-constrained nonlinear systems
Applied Numerical Mathematics
STRSCNE: A Scaled Trust-Region Solver for Constrained Nonlinear Equations
Computational Optimization and Applications
Numerical Methods for Unconstrained Optimization and Nonlinear Equations (Classics in Applied Mathematics, 16)
Truncated regularized Newton method for convex minimizations
Computational Optimization and Applications
On the convergence of an inexact Newton-type method
Operations Research Letters
Accurate surface embedding for higher order finite elements
Proceedings of the 12th ACM SIGGRAPH/Eurographics Symposium on Computer Animation
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We consider the problem of finding a solution of a constrained (and not necessarily square) system of equations, i.e., we consider systems of nonlinear equations and want to find a solution that belongs to a certain feasible set. To this end, we present two Levenberg-Marquardt-type algorithms that differ in the way they compute their search directions. The first method solves a strictly convex minimization problem at each iteration, whereas the second one solves only one system of linear equations in each step. Both methods are shown to converge locally quadratically under an error bound assumption that is much weaker than the standard nonsingularity condition. Both methods can be globalized in an easy way. Some numerical results for the second method indicate that the algorithm works quite well in practice.