Am algorithm for constrained interpolation
SIAM Journal on Scientific and Statistical Computing
SIAM Journal on Scientific and Statistical Computing
A nonsmooth version of Newton's method
Mathematical Programming: Series A and B
Convergence analysis of some algorithms for solving nonsmooth equations
Mathematics of Operations Research
Partially finite convex programming, part II: explicit lattice models
Mathematical Programming: Series A and B
Convergence of the BFGS Method for LC1 Convex Constrained Optimization
SIAM Journal on Control and Optimization
Solution of monotone complementarity problems with locally Lipschitzian functions
Mathematical Programming: Series A and B - Special issue on computational nonsmooth optimization
Complementarity and nondegeneracy in semidefinite programming
Mathematical Programming: Series A and B
A dual approach to constrained interpolation from a convex subset of Hilbert space
Journal of Approximation Theory
Regularization of P0-Functions in Box Variational Inequality Problems
SIAM Journal on Optimization
Semismooth Matrix-Valued Functions
Mathematics of Operations Research
A Dual Approach to Semidefinite Least-Squares Problems
SIAM Journal on Matrix Analysis and Applications
Least-Squares Covariance Matrix Adjustment
SIAM Journal on Matrix Analysis and Applications
A Quadratically Convergent Newton Method for Computing the Nearest Correlation Matrix
SIAM Journal on Matrix Analysis and Applications
Implementation of a primal—dual method for SDP on a shared memory parallel architecture
Computational Optimization and Applications
Constraint Nondegeneracy, Strong Regularity, and Nonsingularity in Semidefinite Programming
SIAM Journal on Optimization
Correlation stress testing for value-at-risk: an unconstrained convex optimization approach
Computational Optimization and Applications
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In this paper, we consider the least squares semidefinite programming with a large number of equality and inequality constraints. One difficulty in finding an efficient method for solving this problem is due to the presence of the inequality constraints. In this paper, we propose to overcome this difficulty by reformulating the problem as a system of semismooth equations with two level metric projection operators. We then design an inexact smoothing Newton method to solve the resulting semismooth system. At each iteration, we use the BiCGStab iterative solver to obtain an approximate solution to the generated smoothing Newton linear system. Our numerical experiments confirm the high efficiency of the proposed method.