On finding supernodes for sparse matrix computations
SIAM Journal on Matrix Analysis and Applications
On the formulation and theory of the Newton interior-point method for nonlinear programming
Journal of Optimization Theory and Applications
An Interior-Point Algorithm for Nonconvex Nonlinear Programming
Computational Optimization and Applications - Special issue on computational optimization—a tribute to Olvi Mangasarian, part II
Computational Optimization and Applications
Solving elliptic control problems with interior point and SQP methods: control and state constraints
Journal of Computational and Applied Mathematics - Special issue on SQP-based direct discretization methods for practical optimal control problems
Computational Optimization and Applications
An Interior Point Algorithm for Large-Scale Nonlinear Programming
SIAM Journal on Optimization
Parallel Computing - Special issue: Parallel computing in numerical optimization
Iterative Methods for Sparse Linear Systems
Iterative Methods for Sparse Linear Systems
Preconditioning Indefinite Systems in Interior Point Methods for Optimization
Computational Optimization and Applications
Inner solvers for interior point methods for large scale nonlinear programming
Computational Optimization and Applications
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This paper is concerned with the numerical solution of a Karush---Kuhn---Tucker system. Such symmetric indefinite system arises when we solve a nonlinear programming problem by an Interior-Point (IP) approach. In this framework, we discuss the effectiveness of two inner iterative solvers: the method of multipliers and the preconditioned conjugate gradient method. We discuss the implementation details of these algorithms in an IP scheme and we report the results of a numerical comparison on a set of large scale test-problems arising from the discretization of elliptic control problems.