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Constraint-reduced predictor corrector IPM for semidefinite programming

version 1.0.0.0 (16.8 KB) by Sungwoo Park
constraint-reduced predictor-corrector interior point method for semidefinite programming

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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 (2020). 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 .

Comments and Ratings (1)

RMS Danaraj

Thank you for sharing this wonderful code !!!
Great work !!!!
RMS Danaraj
salorajan@gmail.cim

MATLAB Release Compatibility
Created with R2010a
Compatible with any release
Platform Compatibility
Windows macOS Linux