American Institute of Mathematical Sciences

2012, 2(1): 129-144. doi: 10.3934/naco.2012.2.129

An efficient algorithm for convex quadratic semi-definite optimization

 1 Department of Mathematics, Shanghai University, Shanghai 200444 2 Department of Mathematics, Zhejiang Sci-Tech University, Hangzhou, 310018, China 3 Department of Mathematics, Zhejiang A&F University, Hangzhou, 311300, China

Received  October 2010 Revised  October 2011 Published  March 2012

We present a full-step interior-point algorithm for convex quadratic semi-definite optimization based on a simple univariate function. The algorithm uses the simple function to determine the search direction and define the neighborhood of central path. The full-step used in the algorithm has local quadratic convergence property according to the proximity function which is also constructed by the simple function. We derive the iteration complexity for the algorithm and obtain the best-known iteration bounds for convex quadratic semi-definite optimization.
Citation: Lipu Zhang, Yinghong Xu, Zhengjing Jin. An efficient algorithm for convex quadratic semi-definite optimization. Numerical Algebra, Control & Optimization, 2012, 2 (1) : 129-144. doi: 10.3934/naco.2012.2.129
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References:
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