Practical methods of optimization; (2nd ed.)
Practical methods of optimization; (2nd ed.)
Trust region algorithms for optimization with nonlinear equality and inequality constraints
Trust region algorithms for optimization with nonlinear equality and inequality constraints
A trust region algorithm for equality constrained optimization
Mathematical Programming: Series A and B
CUTE: constrained and unconstrained testing environment
ACM Transactions on Mathematical Software (TOMS)
Numerical stability and efficiency of penalty algorithms
SIAM Journal on Numerical Analysis
LSQR: An Algorithm for Sparse Linear Equations and Sparse Least Squares
ACM Transactions on Mathematical Software (TOMS)
CUTEr and SifDec: A constrained and unconstrained testing environment, revisited
ACM Transactions on Mathematical Software (TOMS)
Mathematical Programming: Series A and B
An Inexact SQP Method for Equality Constrained Optimization
SIAM Journal on Optimization
Steering exact penalty methods for nonlinear programming
Optimization Methods & Software - Dedicated to Professor Michael J.D. Powell on the occasion of his 70th birthday
An inexact Newton method for nonconvex equality constrained optimization
Mathematical Programming: Series A and B
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We present a line search algorithm for large-scale constrained optimization that is robust and efficient even for problems with (nearly) rank-deficient Jacobian matrices. The method is matrix-free (i.e., it does not require explicit storage or factorizations of derivative matrices), allows for inexact step computations, and is applicable for nonconvex problems. The main components of the approach are a trust region subproblem for handling ill-conditioned or inconsistent linear models of the constraints and a process for attaining a sufficient reduction in a local model of a penalty function. We show that the algorithm is globally convergent to first-order optimal points or to stationary points of an infeasibility measure. Numerical results are presented.