A unified kernel function approach to primal-dual interior-point algorithms for convex quadratic SDO
This paper presents a unified approach to primal-dual interior-point algorithms for the convex quadratic semidefinite optimization (SDO) problem based on the eligible kernel functions. The authors show that every eligible kernel function gives rise to an interior-point algorithm for the convex quadratic semidefinite optimization problem. Iteration bounds for large and small update methods are obtained.
convex quadratic semidefinite optimization
scientific article; zbMATH DE number 5939485
scientific article