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Table 1 Comparison of the CPU times between APGM and Algorithm 1

From: A customized proximal point algorithm for stable principal component pursuit with nonnegative constraint

n

Algorithm

\(\boldsymbol{R_{r} =0.01}\) , \(\boldsymbol{C_{r} =0.01}\)

\(\boldsymbol{R_{r} =0.02}\) , \(\boldsymbol{C_{r} =0.02}\)

\(\boldsymbol{R_{r} =0.03}\) , \(\boldsymbol{C_{r} =0.03}\)

min/avg/max

min/avg/max

min/avg/max

100

APGM

0.4/0.5/0.9

0.9/1.1/1.7

0.9/1.1/1.4

Algorithm 1

0.7/0.9/1.6

1.0/1.4/2.3

1.1/1.1/1.2

150

APGM

1.5/1.8/2.0

2.2/2.4/2.6

2.1/2.2/2.4

Algorithm 1

1.8/2.4/2.9

2.0/2.2/2.4

2.2/2.3/2.5

200

APGM

3.3/4.0/6.6

4.1/4.7/6.5

3.4/4.0/5.0

Algorithm 1

3.6/4.2/5.4

3.8/4.0/4.9

4.0/4.5/5.3

250

APGM

6.3/8.9/18.5

6.2/6.9/8.5

5.4/6.9/9.7

Algorithm 1

6.3/8.0/12.5

6.5/6.7/7.3

7.3/9.6/16.4

300

APGM

10.8/11.7/12.5

9.1/10.5/15.4

8.0/9.8/12.6

Algorithm 1

10.5/11.7/14.0

10.5/11.8/15.6

11.5/14.1/16.1

400

APGM

33.6/36.1/38.6

28.9/30.1/31.2

29.7/30.2/33.5

Algorithm 1

33.6/36.0/38.0

37.8/39.7/41.4

30.9/45.1/47.2

500

APGM

69.6/74.9/83.0

63.1/66.0/67.3

62.1/64.7/66.5

Algorithm 1

79.8/83.7/93.9

86.8/89.5/91.6

90.7/94.5/97.6