Research Article
An Efficient Method for Convex Constrained Rank Minimization Problems Based on DC Programming
Table 3
Numerical results for problems with
and SR = 0.2.
| | Alg | | FR | NS | AT | RE | | FR | NS | AT | RA |
| | DC | 1 | 0.0995 | 10 | 0.30 | | 5 | 0.4875 | 10 | 1.50 | | | SVT | 7 | 1.82 | | 0 | — | — | | FPC | 10 | 3.14 | | 0 | — | — | | FPCA | 10 | 0.12 | | 0 | — | — | | OptSpace | 10 | 0.03 | | 10 | 0.58 | |
| | DC | 2 | 0.1980 | 10 | 0.77 | | 6 | 0.5820 | 6 | 2.82 | | | SVT | 0 | — | — | 0 | — | — | | FPC | 0 | — | — | 0 | — | — | | FPCA | 8 | 0.21 | | 0 | — | — | | OptSpace | 10 | 0.05 | | 9 | 0.97 | |
| | DC | 3 | 0.2955 | 10 | 0.64 | | 7 | 0.6755 | 5 | 3.24 | | | SVT | 0 | — | — | 0 | — | — | | FPC | 0 | — | — | 0 | — | — | | FPCA | 6 | 0.09 | | 0 | — | — | | OptSpace | 10 | 0.12 | | 5 | 1.89 | |
| | DC | 4 | 0.3920 | 9 | 1.16 | | | | | | | | SVT | 0 | — | — | | | | | | | FPC | 0 | — | — | | | | | | | FPCA | 0 | — | — | | | | | | | OptSpace | 10 | 0.22 | | | | | | |
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