Based on the change of , an augmented lagrangian algorithm to solve convex quadratic SDP is proposed. The algorithm’s distinguishing feature is a factorization, the gradient method and an exact linesearch procedure. The convergence of the algorithm is shown. Numerical experiments show that our methods are efficient and robust.

NLPforCQSDP\barparameter.m
...........\coefficient.m
...........\generateDATE.m
...........\genparameter.m
...........\gradient.m
...........\linesearch.m
...........\main1.m
...........\qsdpAL.m
...........\residual.m
...........\v0.m
NLPforCQSDP

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