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Theory and Modern Applications

Table 1 Computational results for compressive sensing

From: Novel forward–backward algorithms for optimization and applications to compressive sensing and image inpainting

m-sparse signal

Methods

N = 1024, M = 512

N = 2048, M = 1024

CPU

Iter

CPU

Iter

m = 50

Algorithm 1.1

17.2860

8219

60.6703

11,237

Algorithm 1.2

15.6780

2941

54.0610

4429

Algorithm 3.1

10.1545

1266

29.8420

1522

m = 60

Algorithm 1.1

30.3607

11,478

82.9704

13746

Algorithm 1.2

20.5542

3700

56.8577

4718

Algorithm 3.1

12.4216

1622

30.7309

1742

m = 70

Algorithm 1.1

39.9470

13,507

97.9897

15191

Algorithm 1.2

21.8114

4079

60.7027

5035

Algorithm 3.1

14.4815

1873

33.8620

1880

m = 90

Algorithm 1.1

112.9716

24,608

124.0622

17,415

Algorithm 1.2

30.4207

5683

72.1555

5793

Algorithm 3.1

24.9734

3121

38.2926

2137