Research Article
An Efficient Method for Convex Constrained Rank Minimization Problems Based on DC Programming
Table 2
Numerical results for problems with
and
.
| | Alg | | FR | NS | AT | RE | | FR | NS | AT | RA |
| | DC | 1 | 0.0988 | 10 | 0.02 | | 6 | 0.5550 | 9 | 0.12 | | | SVT | 10 | 0.11 | | 0 | — | — | | FPC | 9 | 0.13 | | 0 | — | — | | FPCA | 10 | 0.03 | | 0 | — | — | | OptSpace | 10 | 0.01 | | 10 | 0.21 | |
| | DC | 2 | 0.1950 | 10 | 0.03 | | 7 | 0.6388 | 9 | 0.17 | | | SVT | 9 | 0.18 | | 0 | — | — | | FPC | 8 | 0.14 | | 0 | — | — | | FPCA | 10 | 0.02 | | 0 | — | — | | OptSpace | 10 | 0.01 | | 9 | 0.37 | |
| | DC | 3 | 0.2888 | 10 | 0.05 | | 8 | 0.7200 | 7 | 0.28 | | | SVT | 2 | 0.36 | | 0 | — | — | | FPC | 5 | 0.25 | | 0 | — | — | | FPCA | 10 | 0.06 | | 0 | — | — | | OptSpace | 10 | 0.04 | | 9 | 0.82 | |
| | DC | 4 | 0.3800 | 10 | 0.06 | | 9 | 0.7987 | 4 | 0.40 | | | SVT | 1 | 0.62 | | 0 | — | — | | FPC | 1 | 0.25 | | 0 | — | — | | FPCA | 0 | — | — | 0 | — | — | | OptSpace | 10 | 0.05 | | 4 | 1.48 | |
| | DC | 5 | 0.4688 | 10 | 0.08 | | | | | | | | SVT | 0 | — | — | | | | | | | FPC | 0 | — | — | | | | | | | FPCA | 0 | — | — | | | | | | | OptSpace | 10 | 0.09 | | | | | | |
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