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Sieve-SDP: a simple facial reduction algorithm to preprocess semidefinite programs
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DOI：10.1007/s12532-019-00164-4.png)
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Abstract
En 中文
We introduce Sieve-SDP, a simple facial reduction algorithm to preprocess semidefinite programs (SDPs). Sieve-SDP inspects the constraints of the problem to detect lack of strict feasibility, deletes redundant rows and columns, and reduces the size of the variable matrix. It often detects infeasibility. It does not rely on any optimization solver: the only subroutine it needs is Cholesky factorization, hence it can be implemented in a few lines of code in machine precision. We present extensive computational results on several problem collections from the literature, with many SDPs coming from polynomial optimization.
Keywords:
Semidefinite programming
Preprocessing
Strict feasibility
Strong duality
Facial reduction
Polynomial optimization
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