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Efficient Low-Rank Semidefinite Programming with Robust Loss Functions
In real-world applications, it is important for machine learning algorithms to be robust against data outliers or corruptions.
A biconvex optimization for solving semidefinite programs via bilinear factorization
In this paper, we propose to convert SDP into a biconvex problem via the $bilinear$ factorization ($Z$ as $XY^\top$), and while adding the term $\frac{\gamma}{2}||X-Y||_F^2$ to penalize the difference of $X$ and $Y$.
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