A convergence proof for an affine-scaling algorithm for convex quadratic programming without nondegeneracy assumptions

A convergence proof for an affine-scaling algorithm for convex quadratic programming without nondegeneracy assumptions

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Article ID: iaor19941965
Country: Netherlands
Volume: 60
Issue: 1
Start Page Number: 69
End Page Number: 79
Publication Date: Jun 1993
Journal: Mathematical Programming (Series A)
Authors:
Keywords: programming: convex
Abstract:

This paper presents a theoretical result on convergence of a primal affine-scaling method for convex quadratic programs. It is shown that, as long as the stepsize is less than a threshold value which depends on the input data only, Ye and Tse’s interior ellipsoid algorithm for convex quadratic programming is globally convergent without nondegeneracy assumptions. In addition, its local convergence rate is at least linear and the dual iterates have an ergodically convergent property.

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