A structured survey of scalable semidefinite programming covering sparsity, symmetry, low-rank factorization, first-order methods, and conservative LP/SOCP relaxations, with software pointers.
A Two-Step Pre-Processing for Semidefinite Programming
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abstract
In semidefinite programming (SDP), a number of pre-processing techniques have been developed including chordal-completion procedures, which reduce the dimension of individual constraints by exploiting sparsity therein, and facial reduction, which reduces the dimension of the problem by removing redundant rows and columns. This paper suggest that these work in a complementary manner and that facial reduction should be used after chordal-completion procedures. In computational experiments on SDP instances from the SDPLib, a benchmark, and structured instances from polynomial and binary quadratic optimisation, we show that such two-step pre-processing with a standard interior-point method outperforms the interior point method, with or without the traditional pre-processing.
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math.OC 1years
2019 1verdicts
CONDITIONAL 1representative citing papers
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A Survey of Recent Scalability Improvements for Semidefinite Programming with Applications in Machine Learning, Control, and Robotics
A structured survey of scalable semidefinite programming covering sparsity, symmetry, low-rank factorization, first-order methods, and conservative LP/SOCP relaxations, with software pointers.