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Matrix optimization over low-rank spectral sets: stationary points and local and global minimizers. (English) Zbl 1432.90124
Summary: In this paper, we consider matrix optimization with the variable as a matrix that is constrained into a low-rank spectral set, where the low-rank spectral set is the intersection of a low-rank set and a spectral set. Three typical spectral sets are considered, yielding three low-rank spectral sets. For each low-rank spectral set, we first calculate the projection of a given point onto this set and the formula of its normal cone, based on which the induced stationary points of matrix optimization over low-rank spectral sets are then investigated. Finally, we reveal the relationship between each stationary point and each local/global minimizer.
##### MSC:
 90C26 Nonconvex programming, global optimization 90C30 Nonlinear programming 90C46 Optimality conditions and duality in mathematical programming
##### Software:
LowRankModels; Pyglrm; SDPLR
Full Text:
##### References:
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