A weighted full-Newton step primal-dual interior point algorithm for convex quadratic optimization
In this paper a new weighted short-step primal-dual interior point algorithm to solve convex quadratic optimization (CQO) problems. The algorithm uses at each interior iteration afull-Newton step and the strategy of the central to obtain an epsilon-optimal solution of CQO.
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Block coordinate descent (BCD) methods are widely-used for large-scale numerical optimization because of their cheap iteration costs, low memory requirements, amenability to parallelization, and ability to exploit problem structure.
Laradji, Issam +2 more
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