A robust sequential quadratic programming method
Mathematical Programming: Series A and B
On the Global Convergence of a Filter--SQP Algorithm
SIAM Journal on Optimization
A Modified SQP Method and Its Global Convergence
Journal of Global Optimization
A Modified SQP Method with Nonmonotone Linesearch Technique
Journal of Global Optimization
A Globally and Superlinearly Convergent SQP Algorithm for Nonlinear Constrained Optimization
Journal of Global Optimization
A Pattern Search Filter Method for Nonlinear Programming without Derivatives
SIAM Journal on Optimization
Interior Point Methods for Second-Order Cone Programming and OR Applications
Computational Optimization and Applications
A globally convergent primal-dual interior-point filter method for nonlinear programming
Mathematical Programming: Series A and B
Second-Order Cone Programming Relaxation of Sensor Network Localization
SIAM Journal on Optimization
The Q method for second order cone programming
Computers and Operations Research
A line search filter approach for the system of nonlinear equations
Computers & Mathematics with Applications
Second order cone programming relaxation for quadratic assignment problems
Optimization Methods & Software
A one-step smoothing Newton method for second-order cone programming
Journal of Computational and Applied Mathematics
A class of nonlinear Lagrangians for nonconvex second order cone programming
Computational Optimization and Applications
Distributed sensor network localization using SOCP relaxation
IEEE Transactions on Wireless Communications - Part 1
Joint Transceiver Beamforming in MIMO Cognitive Radio Network Via Second-Order Cone Programming
IEEE Transactions on Signal Processing
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In this paper, we consider the nonlinear second-order cone programming problem. By combining an SQP method and filter technique, we present a trust region SQP-filter method for solving this problem. The proposed algorithm avoids using the classical merit function with penalty term. Furthermore, under standard assumptions, we prove that the iterative sequence generated by the presented algorithm converges globally. Preliminary numerical results indicate that the algorithm is promising.