Constraint reduction is an essential method because the computational cost of the interior point methods can be effectively saved. Park and O'Leary proposed a constraint-reduced predictor-corrector algorithm for semidefinite programming with polynomial global convergence, but they did not show its superlinear convergence. We first develop a constraint-reduced algorithm for semidefinite programming having both polynomial global and superlinear local convergences. The new algorithm repeats a corrector step to have an iterate tangentially approach a central path, by which superlinear convergence can be achieved.
Sungwoo Park (2022). Constraint-reduced predictor corrector IPM for semidefinite programming (https://www.mathworks.com/matlabcentral/fileexchange/54117-constraint-reduced-predictor-corrector-ipm-for-semidefinite-programming), MATLAB Central File Exchange. Retrieved .
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