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Constraint-reduced predictor corrector IPM for semidefinite programming

version (16.8 KB) by Sungwoo Park
constraint-reduced predictor-corrector interior point method for semidefinite programming


Updated 22 Nov 2015

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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.

Cite As

Sungwoo Park (2022). Constraint-reduced predictor corrector IPM for semidefinite programming (, MATLAB Central File Exchange. Retrieved .

MATLAB Release Compatibility
Created with R2010a
Compatible with any release
Platform Compatibility
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