CUTE: constrained and unconstrained testing environment
ACM Transactions on Mathematical Software (TOMS)
Mathematical Programming: Series A and B
An Interior-Point Algorithm for Nonconvex Nonlinear Programming
Computational Optimization and Applications - Special issue on computational optimization—a tribute to Olvi Mangasarian, part II
Trust-region methods
A D. C. Optimization Algorithm for Solving the Trust-Region Subproblem
SIAM Journal on Optimization
On Smoothing Methods for the P0 Matrix Linear Complementarity Problem
SIAM Journal on Optimization
Solving a Class of Linearly Constrained Indefinite QuadraticProblems by D.C. Algorithms
Journal of Global Optimization
Interior-Point Methods for Massive Support Vector Machines
SIAM Journal on Optimization
A Globally and Locally Superlinearly Convergent Non--Interior-Point Algorithm for P0 LCPs
SIAM Journal on Optimization
Iterative Methods for Sparse Linear Systems
Iterative Methods for Sparse Linear Systems
Large-Scale Molecular Optimization from Distance Matrices by a D. C. Optimization Approach
SIAM Journal on Optimization
Numerical Optimization: Theoretical and Practical Aspects (Universitext)
Numerical Optimization: Theoretical and Practical Aspects (Universitext)
Complementarity: Applications, Algorithms and Extensions (Applied Optimization)
Complementarity: Applications, Algorithms and Extensions (Applied Optimization)
A branch and reduce approach for solving a class of low rank d.c. programs
Journal of Computational and Applied Mathematics
Journal of Global Optimization
Properties of two DC algorithms in quadratic programming
Journal of Global Optimization
A DC programming approach for solving the symmetric Eigenvalue Complementarity Problem
Computational Optimization and Applications
Exact penalty and error bounds in DC programming
Journal of Global Optimization
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In this paper, we provide a new regularization technique based on DC programming and DC Algorithms to handle indefinite Hessians in a primal-dual interior point context for nonconvex quadratic programming problems. The new regularization leads automatically to a strongly factorizable quasidefinite matrix in the Newton system. Numerical results show the robustness and the efficiency of our approach compared with LOQO. Moreover, in our computational testing, our method always provided globally optimal solutions to those nonconvex quadratic programs that arise from reformulations of linear complementarity problems.