Research Article
An Efficient Method for Convex Constrained Rank Minimization Problems Based on DC Programming
Table 4
Numerical results for problems with
and SR = 0.3.
| | Alg | | FR | NS | AT | RE | | FR | NS | AT | RA |
| | DC | 1 | 0.0663 | 10 | 0.19 | 1.49E − 04 | 7 | 0.4530 | 10 | 0.61 | 3.64E − 04 | | SVT | 10 | 0.68 | 1.66E − 04 | 0 | — | — | | FPC | 10 | 1.33 | 4.47E − 04 | 0 | — | — | | FPCA | 10 | 0.17 | 6.12E − 05 | 0 | — | — | | OptSpace | 10 | 0.05 | 1.29E − 04 | 10 | 0.47 | 3.39E − 04 |
| | DC | 2 | 0.1320 | 10 | 0.20 | 1.69E − 04 | 8 | 0.5120 | 10 | 0.87 | 4.70E − 04 | | SVT | 8 | 1.39 | 1.66E − 04 | 0 | — | — | | FPC | 10 | 2.18 | 4.84E − 04 | 0 | — | — | | FPCA | 10 | 0.16 | 7.68E − 05 | 0 | — | — | | OptSpace | 10 | 0.04 | 1.48E − 04 | 10 | 0.63 | 3.67E − 04 |
| | DC | 3 | 0.1970 | 10 | 0.25 | 1.92E − 04 | 9 | 0.5730 | 9 | 1.03 | 4.85E − 04 | | SVT | 4 | 1.77 | 1.23E − 04 | 0 | — | — | | FPC | 9 | 4.32 | 5.82E − 04 | 0 | — | — | | FPCA | 9 | 0.19 | 1.21E − 04 | 0 | — | — | | OptSpace | 10 | 0.07 | 1.98E − 04 | 10 | 1.40 | 4.87E − 04 |
| | DC | 4 | 0.2613 | 10 | 0.32 | 2.30E − 04 | 10 | 0.6333 | 8 | 1.36 | 6.21E − 04 | | SVT | 4 | 9.82 | 4.67E − 04 | 0 | — | — | | FPC | 7 | 5.02 | 6.59E − 04 | 0 | — | — | | FPCA | 10 | 0.18 | 6.63E − 04 | 0 | — | — | | OptSpace | 10 | 0.09 | 2.08E − 04 | 10 | 1.80 | 5.97E − 04 |
| | DC | 5 | 0.3250 | 10 | 0.37 | 2.72E − 04 | 11 | 0.6930 | 9 | 2.19 | 7.29E − 04 | | SVT | 0 | — | — | 0 | — | — | | FPC | 1 | 8.18 | 7.34E − 04 | 0 | — | — | | FPCA | 10 | 0.10 | 2.20E − 04 | 9 | — | — | | OptSpace | 10 | 0.19 | 2.67E − 04 | 5 | 4.11 | 7.05E − 04 |
| | DC | 6 | 0.3880 | 10 | 0.47 | 3.00E − 04 | 12 | 0.7520 | 6 | 2.83 | 8.79E − 04 | | SVT | 0 | — | — | 0 | — | — | | FPC | 0 | — | — | 0 | — | — | | FPCA | 0 | 0.09 | 2.20E − 04 | 0 | — | — | | OptSpace | 10 | 0.25 | 3.25E − 04 | 6 | 6.44 | 8.47E − 04 |
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